Does the scarcity of female programmers, suggest that men are more intelligent than women?

Does the scarcity of female programmers, suggest that men are more intelligent than women?

Developing software is simultaneously artistic and scientific, which accounts for its appeal for some of the smartest and intuitive people on the planet

There is a generally observable and most likely a statistically provable scarcity of women in the programmer community.

Would it be right to conclude from this that men are generally more intelligent than women? If so, what's the cause of this? And if not, why?

The short answer: No, sex differences in professions is not a good basis for judging the intelligence of males and females.

I would like to address some of the assumptions and misconceptions in the question. First, I would like to deconstruct the question, and then answer it.

Deconstructing the question

One of the earlier titles of the question was "Are men more intelligent than women?". It starts with the observation that there are more males who work in areas related to mathematics and programming, therefore males are more intelligent.

I think this is a common bias in humans. People know a lot about their area of expertise and then judge others by their lack of understanding of what they are experts in. To take a stereotypical example, perhaps a female clinical psychologist, doctor, or lawyer may wonder why so many males are mathematicians and programmers. She might think that this is because they lack the intelligence to function effectively in domains that require strong interpersonal skills. I am not defending this point of view either. I merely intend to highlight that to judge others by your own standards of what represents intelligence is problematic.

Answering the question

Have a read of page 91 of "Intelligence: Knowns and Unknowns", which represents the position of a large reputable APA task force of leading intelligence researchers. Summarising a huge literature, males tend to perform much better on visual-spatial intelligence test items such as mental rotation and tracking moving objects. Females often perform better on verbal abilities such as synonym generation and verbal fluency. Overall, there is minimal difference in full-scale IQ.

You could also have a read of Hide's (2005) summary of meta-analytic sex differences across a wide range of cognitive tests.

Here, the author advances a very different view, the gender similarities hypothesis, which holds that males and females are similar on most, but not all, psychological variables.

However, this only addresses mean differences, and there is certainly much greater differences within sexes than between.


  • Neisser, U., Boodoo, G., Bouchard Jr, T. J., Boykin, A. W., Brody, N., Ceci, S. J.,… & Urbina, S. (1996). Intelligence: Knowns and unknowns. American psychologist, 51(2), 77. PDF
  • Hyde, J. S. (2005). The gender similarities hypothesis. American psychologist, 60(6), 581. PDF

No. In developing IQ tests for intelligence science has found that age not sex is the key difference between groups of people for which non-biased conclusions can be drawn.

Most IQ tests are constructed so that there are no differences between the average (mean) scores of females and males. Areas where differences in mean scores have been found include verbal and mathematical ability.

-Wikipedia Sex differences in psychology

If anything modern research purports that women have an insignificant half point above men.

To think that men and women would differ should be obvious (specialization to differing tasks). To think that one group would be better or worse overall would be counter-intuitive (survival would be worsened).

When I was in college, the Engineering school with the highest proportion of women (46%) was Computer Science. I don't know why. I think it is also the Engineering field with the most women actually employed. When I worked as a programmer in a company with about 100 employees, about 40% were programmers, and most of those were women. Some of them were among the most intelligent people I have ever known.

I think that no general statements about intelligence relative to male vs female will hold up overall. I do know that females have much greater touch sensitivity, as proven by research. But that would make sense in terms of evolution. Computer programming has not been around very long for male-female differences to arise in the brain. (Check back in a million years.)

Toward a Solution

Many institutions of higher education are reviewing and reforming their policies and practices in response to the national focus on women’s participation in science and shortages in the science and technology workforce resulting from national security measures introduced after September 11, 2001, which have made it difficult for highly skilled non-U.S. workers to get U.S. visas. Attracting women to science and high-tech entrepreneurship and then retaining them will require changing the culture of science to make it more family-friendly and inviting.

First, both men and women must recognize that women who want families do not have the luxury of waiting to have children until they have established their careers. Grant-making organizations should allow all applicants to allocate grant money toward family care, including child and elder care. Clare Booth Luce Professorships, funded by the Henry Luce Foundation and designed to advance the careers of women in science, engineering, and mathematics, provide a model for doing so.

Second, STEM departments at U.S. universities should incorporate marketing, finance, management, and other business training into graduate education. Interview research reveals that high-tech employers seek STEM workers who understand project management, leadership, and business skills, such as the ability to read financial statements and write proposals. Women often do not receive this mentoring in graduate school, just as in the 1970s they often were not mentored in grant writing.

A third solution, uncontroversial and low cost, would be for the government simply to enforce existing antidiscrimination laws, such as the sixth, seventh, and ninth titles of the Civil Rights Act. Men are no longer banning women from their academic laboratories (as was done to Madame Curie until she won her second Nobel Prize) or withholding research funding to support their employment (as was done by the federal funders of the Stanford Linear Accelerator Center during the 1960s). But cultural and institutional biases do creep in to chill the climate for women scientists. For example, A Study on the Status of Women Faculty in Science at MIT, published in 1999, found an unequal distribution of resources between men and women faculty in terms of laboratory space, salary supplements, start-up packages, university funding, and even prize nominations.

Fourth, the NSF’s ADVANCE program should be expanded and redirected. Initiated in 2001 to increase the representation of women in academic science and engineering careers, this modest program has achieved fantastic results at almost thirty U.S. universities. This successful model should be expanded to include other federal agencies such as the National Institutes of Health, the Defense Advanced Research Projects Agency, and the Department of Energy.

Fifth, more aggressively promoting qualified women to science advisory boards, science journal editorial boards, and science policy positions would make them more visible. Mentors need to encourage their women graduate students to assert themselves and to sell their ideas, and they should help introduce them into men-only networks.

Finally, the country as a whole must reject the portrayal of women scientists as a special interest group. Women constitute half of the U.S. population and now earn more than half of the undergraduate degrees in science. Because of the income advantage that STEM skills bring, losing STEM-trained women from the workforce as a result of poor mentoring or a failure to institute family-friendly policies risks resegregating women economically. Moreover, 2006 research by the nonprofit group Engineers Dedicated to a Better Tomorrow documents that women are attracted to science and technology when they see its “specific and tangible contributions to society and in bettering local communities, our nation, and the world.” These sound like exactly the characteristics we need in scientists and engineers to help create a better society. If we fail to rid ourselves of anachronistic cultural biases and outdated policies, we will lose out to countries that are able to do so. But if we succeed in attracting more women to science and technology, we will benefit both women and science.

Sue V. Rosser is dean of the Ivan Allen College and professor of public policy and history, technology, and society at the Georgia Institute of Technology. Her e-mail address is [email protected] Mark Zachary Taylor is assistant professor of international affairs at Georgia Tech. His e-mail address is [email protected]

Declining Numbers of Women in Programming, What Can SO do to Help?

In summary this post is to discuss the link of diminishing numbers of women in programming and the research that reveals this is due to women feeling isolated within such a male dominated field.

I do not speak for all women, but the subset of female programmers who (according to research) find it more difficult than their male counterparts participating in programming communities, whether in a learning, working or online community.

As a community do we want to alleviate this? if so:
Is there anything we can do as a community to alleviate this?

This is just a discussion. It has no purpose except to help bring some issues to light and stimulate discussion.

In part I was prompted to write this after seeing a horrible post on here where the OP used a housewife as a benchmark for stupidity. It had been there for a while. I was very upset and flagged it. The post was deleted.

This in turn prompted me to write this as a suggested question for moderator candidates.

This will probably not be well received, as we are all supposed to be treated with the same brush here and I'm telling you it doesn't work for some groups within our global society. Also, it feels like I'm breaking some unspoken law in the ever tightening coil of political correctness. And this post is focusing on WOMEN, and I feel qualified to speak as a woman, on the experience of being in such a male dominated area, as it is well documented that my feelings are common among my female peers 2. I can not speak for all women, but I can speak for a majority.

I can supply as many references and sources that are required if these links do not stand up to scrutiny.

A quick Google search verifies the diminishing numbers of women in this field.

What many of the users on here do not realise how difficult it is being a woman in this field. Women, by and large are not as confident in their programming skills and do not always understand many of the nuances of such a male dominated culture.

Now this is not the fault of SO, but is worth bearing in mind that this is many women's experiences before they ever land on this site.

Think of it this way. How would you talk to me or any other woman face to face? How would you like people to be addressing you mother, sister, wife or daughter? How we address people based on age and gender does vary. Try and visualise 3 or 4 men standing around talking to a woman. What happens as the discussion gets heated? What if everyone starts yelling. What if the men happen to be in agreement and the woman is outnumbered? Could the woman feel intimidated?

Just because it's online it does not prevent a woman from feeling the same way as if it was face to face. Particularly when people are prone to stalking people around the internet - and that is not specific to men, but it is to try and show how events and fears can be linked within an online interaction.

The fact is we have different ways of relating and different expectations of what is professionalism and what is not. This couple with the fact that online communication is difficult at the best of times, lacking the major cues of communications, intonation, body language and facial expressions.

As a woman I frequently feel intimidated when debating with men online. When I feel threatened, the way I have personally coped with it online is to become aggressive, much like a cat fluffs up its fur. As you can well see from my overreactions in the comments, I am in the wrong.

Now this is not the fault of SO, that's one person's defence mechanism to being vulnerable. One a site like this, we're putting ourselves on the line, to be placed on that spectrum of incompetent->skilled, ignorant->educated, simple-minded->intelligent.

Women are (usually) vulnerable physically in the real world to men. Just by sheer size, nothing bad. And much of our interactions with men (we don't know) is centered around maintaining physical safety. I understand men can be hurt by women, I am not saying women are better than men or that all men are dangerous. If a woman has to walk through a group of men on the street, she's tense. I am trying to convey the woman's psyche in a way that may be helpful to men interacting with women online in a site like this. The pressure is quite intense.

I stick with it, as I love SO and the knowledge is immense and it improves my programming skills. More importantly, as my skills improve (they are by no means brilliant), I want to show myself as a woman with growing experience on the most well-known programming site, and a site that also has a reputation for being rough and tumble for newcomer programmers. I want to do this to help other women.

Now, as mentioned, I am my own worst enemy in how I react in the online environment. When there is an even mix of men and women it is very different than when I am acutely aware of the lack of women, and am usually the only woman in all my sets of interactions on most days on SO. And I do become very defensive when I know there's a bunch of men and if I don't communicate well and then there's discussion, it quickly leads to me being very defensive. Is this your fault? No. Is it my fault, but I'm also doing my best, as it is really difficult. I'm making this appeal, so that the tiniest changes can be made to ease the way for women to come into the field and succeed.

Now to reiterate.. what does this have to do with SO? It's the single largest online programming resource, and it is beneficial as a programmer to be active on this site.

I am trying to express the experience in the hope that this may cause one person, or another and a ripple effect. Even though women are not often comfortable within this type of environment, but we can't give up. I'm hoping to see more high rep female users. Hoping to be one down the track.

Also this discussion encompasses a tolerance towards different groups needs. So many cultural differences and language barriers and by being mindful of this, maybe we can all make a difference with a kind word, and a bit more empathy.

What are the stats on female to male users on the site and within the varying rep brackets?

Do women feel the need to hide their gender on this site like I did?

I was tempted to post an answer, but thought it would be better to elaborate on the question.

To address people who do not agree with the word diminishing. If you click on one of the links I provided or do a google search, it is a fact that the numbers of women in the field are diminishing.

To address the people who suggest I am asking to reinvent SO or criticising SO. Where have I done this?

I have asked people to be mindful and not once do I claim women to be the only human beings to have struggles in life. I am merely addressing the declining numbers of women in the field on the largest programming site in the world. Using my personal experience and the research which indicates why many women are leaving or their experience of learning and working in the field. As research suggests, my experience is typical of the norm.

This type of discussion can be applied to many sub groups of our greater community, I have chosen to discuss this particular subject. My choosing to discuss this does not in any way imply it is more important that other people's concerns.

It is a straw man argument to suggest I asked for positive discrimination, or to change SO, or have singled women out as the only people to struggle with any of the above issues. It is glib and serves only to side step the actual issue being addressed here.

There are a set of issues facing this subset of people.

Let's clarify or TLDR

1. FACT: This is a discussion, not an answerable question I threw out questions to stimulate a discussion

2. FACT: Women are diminishing in this field

3. FACT: There is a significant pool of reasons for this addressed above

4. FACT: This is the largest global programming resource community

5. Statistics and norms are just that, this is not every woman's experience. I am using research to be a voice for a statistical significant portion of the population. This is not ALL women's experience

6. Let's discuss this

I have stated clearly I love SO and the fact I have posted here shows I am aware of how influential we can be as a community to make positive contributions globally. If this bring up other struggles/issues you feel are important, feel free to discuss these here also, but other struggles/issues do not lessen this struggle/issue. There are many intelligent thoughtful people here and I am delighted with the response.

There are many differences between genders, cultures, etc. We cannot reasonably expect everyone to be blended sheep, but we can have a reasonable expectation of what is acceptable community behaviour. But what defines that can also be subjective.

This post shows that the gender differences are not necessarily malicious or even intentional. The point is not to assign blame, but to see what can be done to improve things, for both women and men. Which means an effort on both parts.

The developer surveys and this blog post raise relevant data and information to this topic.

From Scarcity to Visibility: Gender Differences in the Careers of Doctoral Scientists and Engineers (2001)

&hellipattitudes have come a long way since F.Y. Edgeworth worried about whether women should receive equal pay for equal work&hellip

&mdashNancy M.Gordon, et al., American Economic Review, 1974 1


Perhaps the most basic way to contrast the differing career outcomes of men and women in science and engineering is by comparing their salaries. Salary reflects both the type of employment obtained and success in meeting the goals associated with the position held. As such, salary is a form of recognition for professional contributions and a measure of worth in the scientific community. Merton (1973 reprinted from 1942) argues that there is a strong presumption in science that recognition, including monetary rewards, should be determined on the basis of universalistic criteria related to scientific achievement. To the extent that female scientists and engineers receive fewer financial rewards than men for comparable achievements, their work is undervalued and they are underpaid.

Studies of gender differences in salary for scientists and engineers can be divided into two groups. The first group examines salaries within a

Gordon, Morton, and Braden (1974) discussing Edgeworth&rsquos presidential address to the British Association in 1922.

single academic institution (see, for example, Becker and Toutkoushian 1995 Ferber 1974 Fox 1981 Gordon, Morton, and Braden 1974 Hoffman 1976 Katz 1973). Single institution studies have the advantage of more detailed data on each individual and are based on a more complete understanding of the nuances of the local context of employment, but they are limited by the unique characteristics of that institution. A second type of study uses a large sample to study differences across fields, and often across sectors of employment. For example, Ferber and Kordick (1978) examined Ph.D.s in all fields with degrees from 1958&ndash63 and 1967&ndash72. Ahern and Scott (1981), the precursor to our study, examined salaries in five broad fields for Ph.D.s from the 1940s through the early 1970s. Many of these studies of salary are restricted to academics, such as Barbezat (1988), Farber (1977), Gregorio, Lewis, and Wanner (1982), Johnson and Stafford (1979), and Tolbert (1986), or a single field such as Hansen, Weisbrod, and Strauss (1978) or Morgan (1998).

While studies of salary differences for men and women in science and engineering differ widely in their samples, focus, and methodology, each study has found that the average female scientist or engineer earns less than her male counterpart. There have been several proposed explanations for this gap in earnings:

Women earn less because they are less qualified than men. While our analysis in earlier chapters found few gender differences in educational backgrounds, it is still possible that qualifications attained at the completion of formal education may be lower. Due to longer periods out of the labor force, women accumulate fewer years of experience and during periods of absence from S&E their skills may depreciate. Consequently, when women reenter the S&E labor force they will earn a lower salary than at the time of exit and will have foregone the salary increases due to accumulated experience. In anticipation of time out of the labor market, women may choose to invest less in on the job training or employers may invest less in female employees. Lower investment in training early in the career will produce lower future female earnings (Duncan and Hoffman 1979). Or, even with similar education and experience, women may be less productive than men in the scientific workplace. See Cole and Zuckerman (1984), Long (1992), and Xie and Shauman (1998) for a review of the literature on gender differences in productivity.

Cumulative advantage, as defined by Merton (1973 reprinted from 1942), suggests that men are the beneficiaries of gender inequities early in the career and that these early advantages are magnified over time. Even if salary is based entirely on productivity, early disadvantages in employment for women may lead to a pay gap that will grow over the course of their careers.

There may be crowding of women into certain subfields either because of choice, social norms and mentoring, or entry barriers to other subfields. Because salaries are the result of interactions between supply and demand, increases in supply will put downward pressure on wages in these more female friendly subfields. See Bergman (1974) for a general treatment of this phenomenon.

The theory of comparable worth (Bellas 1994) posits that fields that employ a higher proportion of women pay lower salaries because women&rsquos work is devalued by society (Treiman and Hartmann 1991). According to this theory, the suppressing effects of gender composition occurs after controlling for economic factors that affect salaries.

Finally, and perhaps most controversially, female scientists and engineers may receive less pay than men for equal work as a result of subtle or blatant discrimination by employers. This discrimination may take the form of lower wages for women doing the same work as men at all levels of experience. For example, Bellas (1994) and Ahern and Scott (1981) found that the effects of experience on salary were larger for men than women, indicating that men are compensated more than women for any given level of experience. Discrimination may also be reflected in society&rsquos tendency to devalue women&rsquos work, paying lower salaries in fields where large numbers of women work (see point 4 above). Or, discrimination may come in the form of barriers to entry into certain prestigious subfields or jobs resulting in crowding of women into less prestigious, lower paying alternatives.

In this chapter we use data from four years of the SDR to examine the extent and causes of gender differences in salaries. We begin by describing the gross gender differences in salaries without controls for characteristics of either individuals or their employers. We find that men have had a nearly constant 20 percent advantage in salary during the 23 years from 1973 to 1995. To understand why men receive higher salaries and why there has not been an improvement, we add controls for variables that have been suggested by prior research. This is done initially by simply comparing the median salaries of men and women in, for example, the same fields or with the same year of Ph.D. To control simultaneously for a large number of factors, we estimate a series of multiple regressions. The differing characteristics of men and women, such as in experience and field of study, can explain much of the gross gender difference in salary. However, even with numerous controls, gender differences in salary remain. Reasons for these differences are discussed in the summary.

Methodological Issues

Salary data from 1973, 1979, and 1989 were converted to 1995 dollars using adjustment factors for inflation from the U.S. Census Bureau (1999). Multiple regression was used to estimate salaries for men and women after controlling for a large number of variables simultaneously. The effects of the control variables were allowed to differ by gender. In these regressions, the dependent variable is the natural log of salary in 1995 dollars. Since raises are generally based on a percentage increase, a loglinear model provides a better fit. See Hodson (1985) and Becker and Toutkoushian (1995) for further details. A loglinear model predicts the log of income for a given set of characteristics. Since an unbiased estimate of the predicted income (as opposed to the log of income) cannot be computed by simply taking the exponential of the predicted log income, we use Duan&rsquos (1983) nonparametric smearing estimator to compute predicted incomes. For additional details, see Chapter 2.


Figure 7&ndash1 plots the median salaries of men and women in the full time, year-round U.S. labor force and for our sample of full time scientists and engineers for the years of the SDR used in our report. 2 Doctoral scientists and engineers, whether male or female, are well-paid professionals who earn substantially more than the average worker in the U.S. economy. The median salaries of male scientists and engineers have remained about 100 percent higher than those of full-time men in the general population, while the median income of female scientists and engineers have declined from being 200 percent greater than those of women in the general population in 1973 to around 150 percent greater in later years. This decline for doctoral women corresponds to a rise in income for women overall in the U.S. labor force while the real income of women in S&E declined slightly (Figure 7&ndash2).

Since 1973 the median income of male scientists and engineers has been approximately 20 percent higher than the median salary of female scientists and engineers, as shown by Figure 7&ndash1. While large, the earnings gap between male and female doctoral scientists and engineers is much smaller than the gap in the entire U.S. labor force, which would be

For scientists and engineers, we plot the median salary for those employed full time. Data for the U.S. labor force were compiled by the U.S. Census for people 15 and over beginning with March 1980 and people 14 years and over as of March of the following year for previous years. Between 1974 and 1976, wage and salary income were restricted to civilian workers.

FIGURE 7&ndash1 Median incomes in 1995 dollars for full-time, year-round workers in U.S. labor force and for full-time scientists and engineers, by gender.

FIGURE 7&ndash2 Percent greater median income for full-time, year-round workers in U.S. labor force and for full-time scientists and engineers.

expected given that male and female scientists and engineers are more homogenous in their characteristics than are men and women in the general population. Further, the gender gap in earnings is smaller than that for other female professionals (e.g., physicians, executives) or for scientific occupations that require less than a doctorate, such as technicians and programmers (U.S. Department of Labor-Women&rsquos Bureau 1994). However, there has been no sustained improvement in the salary disadvantage for doctoral women in S&E during the 22 years since 1973, while there has been a steady improvement in salaries for women relative to men among full-time, year-round workers in the U.S. population. According to the National Commission on Pay Equity (1996), the shrinking gap is due to the gains women have made in real wages relative to men as a result of increasing years of work experience, increasing equality of education, improved market skills, and the decreased number of high-paying jobs for men. Men&rsquos real wages (in constant dollars adjusted for inflation) drifted downward, while women&rsquos real wages increased.

While gross gender differences in salaries for scientists and engineers have not narrowed since 1973, salary is the outcome of a stratification process that involves many steps, each of which is associated with differences in pay. Earlier chapters showed that due to the increasing entry of women in recent years, female scientists and engineers are on average younger than their male counterparts. Accordingly, we would expect the younger women to earn less. Further, there are gender differences in field of study, sector of employment, and primary work activity. Each of these dimensions of the career is associated with differences in salary and we find generally that women are more likely to be in positions associated with lower salaries. In the remainder of this chapter, we decompose the overall gender differences in salaries, attempting to determine the degree to which men and women with similar characteristics are paid differently.


While there has been no improvement since 1973 in the pay discrepancy between the average male and female scientist or engineer, we know from Chapters 3 and 4 that the average professional age of women is less than that of men. Since salary is strongly affected by years of experience (Ahern and Scott 1981), even if women were compensated in the same way as men, we would expect the average salary for the younger population of women to be lower than that of men. If, however, men had a slight salary advantage at the start of the career, this small difference in starting salary would multiply over time since raises are often calculated on a percentage basis. Further, if women have more interruptions after the

Ph.D., this loss of experience would lead to increasing gender differences over time. Ferber and Kordick (1978) found such an increase in a study of Ph.D.s from 1958&ndash63 and 1967&ndash72, and found convergence in income after women reentered the labor force.

Panel A of Figure 7&ndash3 plots the median salaries of men and women in 1973 by the number of years since the Ph.D. The median salary in any given year is a 5-year average centered on that year. At the start of the career, men are making 12 percent more than women, compared to the 22 percent gross difference we found when the different age structures for men and women were ignored. The gender difference in salary increases steadily to 20 percent in year 15. For the next 10 years, there is an overall increase, although there are substantial fluctuations due to the small number of women with Ph.D.s from the years prior to 1958. Panel B plots similar data for 1995. The first thing to note is that the salaries for both men and women are lower at all stages of the career compared to those in 1973. Since data in both figures are in 1995 dollars, this documents a decline in real income for scientists and engineers between 1973 and 1995. Second, in 1995 the gender gap begins at 20 percent in year 1. For later years, the differences in salaries are generally smaller than in 1973, but for all career years men earn at least 10 percent more than women of the same career age.

The conclusions that we can draw from Figure 7&ndash3 are limited since we are not plotting the salaries of the same group of people as they age over the career. Instead, each year of the career corresponds to a different Ph.D. cohort. For example, in 1973 those in year 5 received degrees in the years around 1969 (recall that we are plotting five-year averages), while those in year 10 received degrees in the years around 1965. Cohorts of Ph.D.s from different years are used to approximate what might happen to a cohort from a single year as it progresses through the career. When interpreting results based on these synthetic cohorts, it is impossible to differentiate empirically between alternative explanations of the results. For example, in Panel A it appears that women encounter a &ldquoglass ceiling&rdquo around year 20 while men&rsquos salaries continue to increase. An alternative explanation is that the cohorts of women that received their Ph.D.s more than 20 years earlier faced obstacles earlier in their careers that limited their incomes later in the career. If more recent cohorts do not face these obstacles, their salaries would continue to increase as they age. Using this argument and data for engineering, Morgan (1998) concludes that the &ldquoearning penalties to women are more a matter of when they started their careers than of how long they have worked.&rdquo

Given the limitations of synthetic cohorts and the results of Morgan (1998), it is important to examine what happens to the same cohort of Ph.D.s over time. This is done in Figure 7&ndash4, which plots gender differ-

FIGURE 7&ndash3 Median salaries for women and men, by years since the Ph.D. and year of survey. NOTES: Median salary is computed using a 5-year moving average. Salaries have been converted to 1995 dollars.

FIGURE 7&ndash4 Percent higher salaries for men, by Ph.D. cohort and year of survey. NOTES: Numbers at the top of each bar are the average professional age of a given cohort in a given year of the survey. There are no bars for the 1979&ndash88 cohort in 1973 and 1979, or for the 1989&ndash94 cohort in 1995 since they had not yet received their degrees.

ences for four cohorts defined by the Ph.D. year at four years of the SDR. Each bar shows the percent higher median salaries for men at a given number of years since the Ph.D. the number at the top of each bar is the approximate career age for that cohort in a given survey year. The set of four bars above shows that the salary advantage for men with degrees from 1959&ndash1968 increased from 17 percent at career year 11 to 19 percent by year 17, with a drop in year 27, ending with a difference of 21 percent in year 33. A steady increase in the salary advantage for men is also seen in the 1969&ndash1978 cohort. By comparing those with similar career ages in different cohorts (e.g., age 11 for the 1956&ndash1968 cohort, age 7 for the 1969&ndash 1978 and 1979&ndash1988 cohorts, and age 5 for the most recent cohort), we find some evidence of a modest decrease in the salary differences for men and women in more recent years.

While these results demonstrate that some of the overall gender difference in salaries can be explained by gender differences in professional age, substantial differences remain. These results are based on years since the Ph.D. Ideally, we would compare salaries of individuals with the same years of full-time professional experience, taking into account interruptions in the career and part time employment. Unfortunately, com-

plete data on years of postdoctoral work experience are not available. Since women are more likely to have interruptions, perhaps due to family obligations, the results given above may over-estimate the age standardized gender differences in salary. For example, career age for women is more likely to over-estimate professional experience than for men. Using data from 1983, Lewis found that career interruptions had equal effects on the salaries of male and female scientists and engineers, but that women were more likely to have interruptions.

The gender differences in salary may also be accounted for by gender differences in other dimensions that affect salary, such as field and type of employment. These dimensions of the career and their effects on salary are now considered.


The link between a field&rsquos sex makeup and its salary level led us to ask whether more female fields pay less partly because their practitioners are mostly women.

&mdashMarcia L.Bellas and Barbara F.Reskin, Academe, 1994 3

Fields differ substantially in the median salaries received by Ph.D.s employed in those fields, as shown in Figure 7&ndash5. Engineers have the highest median income, followed by physical scientists, with mathematicians, life scientists, and social/behavioral scientists following. Field differences have been increasing since 1973, confirming the results of Bellas (1997). For example, in 1973 the median salary in engineering was 8 percent greater than in the social and behavioral sciences by 1995 the difference was over 20 percent.

While Johnson and Stafford (1979) found no discernable pattern of field differences in salary, a series of papers by Bellas and collaborators (Bellas 1993, 1994 Bellas and Reskin 1994) demonstrated that fields employing higher proportions of women pay lower salaries. Her work is based on the concept of comparable worth that argues that since women&rsquos work is devalued by society (Treiman and Hartmann 1991), occupations that are predominantly female receive lower compensation. A simple labor market supply and demand framework can also explain this phenomenon. With the influx of women into science, certain fields saw more absolute growth of employees than others, possibly due to free choice of entering women or to entry barriers imposed to prevent female entry into other fields. In particular, psychology, life sciences and the social sciences were the destinations for many female entrants. With the large increases

FIGURE 7&ndash5 Median salaries of full-time employees, by field of Ph.D. and year of survey. NOTE: Salaries have been converted to 1995 dollars.

in supply of employees, and without similar increases in demand, wages were depressed in these fields relative to fields without these large supply increases. Studies of comparable worth have, however, included controls for labor market conditions. For example, Bellas (1994) used the 1984 National Survey of Faculty sponsored by the Carnegie Foundation (1984) and found that the negative effects of gender composition persisted after control for individual characteristics and labor market conditions.

For 1989 and 1995, Figure 7&ndash6 shows the negative relationship between the percent of Ph.D.s who are female in the full-time labor force of a field and the median salary for that field. There was a weaker relationship in 1973 and 1979 (not shown) since there was little variation in the percent women among fields. Clearly, women are more frequently found in those fields with the lowest salaries. For example, women are much less likely to get degrees in the more highly paid field of engineering and much more likely to obtain degrees in the social and behavioral sciences. There are also differences in subfields. For example, women are much less likely to obtain a doctorate in economics, where salaries are higher, than in anthropology, where they are lower.

While comparable worth suggests that both men and women, not just women, earn less in those fields where there are proportionally more women, our data suggest that women receive less than men even within lower paying fields. Figure 7&ndash7 shows that men have higher salaries in all fields in each of the years examined. However, with the exception of the social and behavioral sciences, there has been a within field decline in the

FIGURE 7&ndash6 Relationship between median salary and percent female, by field and year of survey. NOTE: Eng=engineering Phy=physical sciences Mth=mathematics Lif=life sciences Med=medical sciences SB=social and behavioral sciences.

FIGURE 7&ndash7 Percent higher salaries for men, by field and year of Ph.D. NOTE: There were too few women in engineering in 1973 to make an estimate.

salary advantage for men. In the social and behavioral sciences, men have had a nearly constant 10 percent salary advantage. Thus, women are most likely to have degrees in the broad field that pays the least and in which salary advantages for men have persisted longest. Keep in mind, however, that these figures do not control for professional age.

Regardless of the explanation, women are more frequently found in those fields with the lowest salaries. Overall, field differences accounts for a significant proportion of the gross differences in salary that were documented in the last section.


Figures 7&ndash8 and 7&ndash9 plot median salaries by sector of employment and primary work activity. In each year the salaries are highest in industry, which in large part explains the higher overall salaries of engineers. While in 1973 the median salary in government was close to that in industry, since 1973 government salaries for Ph.D.s have dropped significantly relative to those in industry. Salaries are lowest in academia, where women are most likely to work. Even larger salary differences exist among work activities, as shown in Figures 7&ndash10 and 7&ndash11. The highest salaries are in management, due in large part to managers having more work experience than the average Ph.D. Salaries drop steadily as we move from pro-

FIGURE 7&ndash8 Median salaries by sector of employment and year of survey.

FIGURE 7&ndash9 Median salaries by primary work activity and year of survey.

FIGURE 7&ndash10 Percent higher median salaries for male Ph.D.s, by sector of employment and year of survey.

FIGURE 7&ndash11 Percent higher median salaries for male Ph.D.s, by primary work activity and year of survey.

duction work to applied research, and finally to the lowest salaries for those who are teaching. Overall, differences in salaries by sector and activity are important for understanding gender differences in salaries since women are more likely to be employed in those sectors that pay less and in work activities associated with lower salaries.

There are also differences among sectors and work activities in the degree to which men receive higher salaries than women. Figure 7&ndash10 shows that the salary advantages for men are greatest in the nonprofit sector, with a steady increase from 23 percent in 1973 to 32 percent in 1995. Differences are smallest in government, with a small increase between 1973 and 1995, including a spike to nearly 25 percent in 1979. In both industry and academia, there has been an overall increase in gender differences, although there is evidence of a decrease between 1989 and 1995. Gender differences also vary by work activity, as shown by Figure 7&ndash11. Differences are largest in management, production, and basic research, with smaller differences in teaching and applied research. While there is no clear trend over time, it is important to keep in mind that these figures do not control for gender differences in professional age.


The results so far have controlled for only a single factor at a time (e.g., professional age, sector). But, many key dimensions of the career are interrelated. For example, employment in industry is more likely in engineering and less likely in the social and behavioral sciences. And, within some sectors applied research is more likely, while in other sectors basic research is more common. Interpretation is further complicated since there are significant gender differences in years of professional experience with increasing entry of women occurring at different rates across fields and sectors. Accordingly, to more fully understand gender differences in salary it is necessary to control for these simultaneously. In this section we use regression to examine gender differences in salary after controlling for multiple dimensions of the career. Our strategy is to estimate separate regressions for men and women, which allows the effects of each variable to differ by sex. For each pair of regressions, one for men and a second for women, the predicted salaries for men and women are computed for the combined male and female average levels of the control variables in the equation. 4 These predictions are used to compute the percentage differ-

Since the regressions are nonlinear, predicted values are computed using a nonparametric smearing estimator (Duan 1983). The regressions includes scientists and engineers who are working full time in any sector. Professional age is included by adding years since the Ph.D. and the square of years since the Ph.D., allowing a nonlinear effect of professional age.

ence in the salaries of men and women. Additional variables are added to the regressions and the advantage in salary for men is computed after controls for the additional variables. See Chapter 2 for further details.

Figure 7&ndash12 shows changes in the salary advantage for men as additional variables are added cumulatively to the regression. The two panels present the same information organized to highlight different aspects of the results. The first set of bars in Panel A plots the percent higher salaries for men when only the gender of the individual is used to predict salary. As shown earlier, there is no consistent pattern over time, with men earning between 22 percent and 26 percent more than women. For the second set of bars, career age is added to the regression. Gender differences drop only 2 points in 1973, with drops of between 7 and 10 percentage points in later years. By 1995 the percentage advantage for men has decreased to 13 points after controlling for differences in career age. Keep in mind that we had to use career age rather than years of full-time experience due to missing data for the experience variable. Since women have more time lost to interruptions, we expect that gender differences would have been even smaller if controls for experience were used. The third set of bars adds field of doctoral study, reducing the adjusted gender difference by only 1 point in 1973, with decreases of over 5 points in 1989 and 1995. The male salary advantage continues to drop as controls for sector and primary work activity are added.

With all controls added, the advantage for men was cut in half to 14 percent in 1973 and 1979. In 1989, the advantage was reduced an additional two-thirds to slightly below 10 percent and by 1995 the advantage for men was further reduced to slightly above 5 percent, a drop of three-quarters. After adding controls for differences in background and work experience, a steady decrease over time in the salary advantage for men is found.

Bayer and Astin (1975) argued that the explained variation (i.e., R 2 or coefficient of determination) in salary regressions for women should be smaller than for men. Their argument was that the salaries of women are more strongly affected by discrimination and consequently would not be explained by other variables such as field or years of experience. It is also likely that the careers of women are less predictable than those of men due to a greater number of career interruptions. Figure 7&ndash13 shows that this was clearly the case in 1973 and 1979, but that the difference has declined and is nearly eliminated by 1995. There has also been a steady decrease in the amount of variation that can be explained by the structural variables included in our models. This decrease in what can be explained may reflect the changes in the scientific and engineering labor market that have occurred since 1973.

FIGURE 7&ndash12 Effects of age, field, sector, and primary work activity on gender differences in salary. NOTE: Each bar indicates the percent difference between male and female salaries. Gender Only is the percentage difference in mean salaries +Age adds controls for professional age and age squared +Field adds dummy variables for the field of Ph.D. +Sector adds dummy variables for the sector of employment and +PWA adds controls for primary work activity.

FIGURE 7&ndash13 Explained variation in salary regressions, by sex and year of survey.


The effects of age, field, and work activity may differ by sector of employment. For example, the salary advantage for men in engineering may be larger in one sector than another. To allow for this possibility, a series of regressions was run for each sector separately for industry, government, and academia there were too few cases for separate analyses of those working in the nonprofit sector.

Figure 7&ndash14 shows the percentage difference in salaries for men and women in industry after controlling for age, field, and work activity. With all controls, shown by the set of bars labeled &ldquo+PWA&rdquo, the higher salaries for men are reduced from an 18 percent to a 7 percent advantage in 1973 in 1979 the male advantage was over 15 percent even with controls. By 1995 there was a substantial reduction to an adjusted difference of less than 5 percent. These results are consistent with Vetter&rsquos (1992) finding that there has been convergence in the salaries of doctoral chemists in industry.

Figure 7&ndash15 presents similar data for those employed in government. Overall, the salary advantage for men is smaller than that in industry, and by 1995 after controlling for age, field, and sector, women are estimated to have marginally higher salaries than men.

FIGURE 7&ndash14 Gender differences in salary for those with industrial jobs, controlling for age, field, and work activity, by year of survey.

FIGURE 7&ndash15 Gender differences in salary for those with government jobs, controlling for age, field, and work activity, by year of survey.


&hellip[academic] salaries are not of the nature of wages and that there would be a species of moral obliquity in overtly so dealing with the matter.

&mdashT.Veblen, Higher Learning in America, 1918 5

Despite Veblen&rsquos warning of moral delinquency, the majority of studies of the salaries of scientists and engineers are focused on the academic sector, often being further restricted to those with faculty positions. A key advantage to studying the academic sector is that more is known about characteristics of the employing institutions, the work activity, and, to some extent, productivity. These studies include: Bayer and Astin (1968 1975), Becker and Toutkoushian (1995), Ransom and Megdal (1993), Barbezat (1988), and Toutkoushian (1998). Gray (1993) provides a detailed review of statistical analyses of faculty salaries used in court cases. Overall, these and many other studies have concluded that there has been substantial progress in academia in reducing gender differences in salaries. Barbezat (1988) concluded that &ldquosalary discrimination&rdquo in the academic market is less than in other sectors of the economy. In this section, we begin by examining gender differences in salaries among all full-time, doctoral academic employees. We then restrict our analysis to the influential group of tenure-track faculty at research universities.

Figure 7&ndash16 plots the percentage salary advantage for academic men after controlling for key dimensions of the academic career. Analyses are based on doctoral scientists and engineers employed full time in academia, regardless of work activity or type of institution. The two panels present the same information organized first by the variables added to the regression and second by the year of the survey. The first column in Panel A shows the overall gender differences in salaries without any controls. The higher salaries for men increase from 18 percent in 1973 to a high of 24 percent in 1989 before dropping back to 20 percent in 1995. The results labeled &ldquo+Age&rdquo show that the increasing overall differences in academic salaries during this period were due to the younger professional age of women in academia. Controlling for professional age substantially decreases the salary advantage for men, particularly in 1979 and later. By 1995, the advantage for men is reduced to 10 percent. If data on years of experience had been available, these decreases would probably have been even larger. Looking at Panel B, we see that the effects of professional age only became large after 1973 (shown by the large drop from the solid black bar to the adjacent bar). This corresponds to the rapid influx of

From Veblen (1918) page 161, note 1.

FIGURE 7&ndash16 Percentage higher salaries for academic men after controlling for structural variables, by year of survey. NOTE: Gender Only is the percentage difference in mean salaries +Age adds controls for professional age and age squared +Field adds dummy variables for the field of Ph.D. +Carnegie adds dummy variables for Carnegie class of employer +PWA adds controls for primary work activity +Family adds controls for married with young children (not available in 1973).

women into academia during this period. Since women are more likely to have interruptions due to family obligations, our measure of experience as years since the Ph.D. is likely to overestimate the professional experience of women. If we had a measure of years of work experience, the reduction in gender differences in salary would likely be even greater. Ferber and Kordick (1978:227), in a study of Ph.D.s from 1958&ndash1963 and 1967&ndash1971, concluded that &ldquothe relatively lower earnings of highly educated women can be explained largely by their career interruptions&hellip&rdquo She found that once women reentered the labor force on a permanent basis, gender differences in salary were reduced. Unfortunately, more recent data are not available.

In academia, as in other sectors, there are significant salary differences across fields. Feldberg (1984:315) found that in academia, as in science as a whole, faculty in fields where there are proportionately more women receive lower salaries even after controlling for human capital and scientific productivity. Bellas (1994) confirmed this result in several studies that were discussed earlier. Note, however, that women tend to be found least often in those fields in which there is the greatest demand from industry, and accordingly salaries would be expected to be higher. While we confirm the direction of field differences from past research, the magnitudes are small after controlling for differences in years of experience. In 1973 and 1979, controls for broad field resulted in only trivial reductions in gender differences, with somewhat larger reductions of 3 points in 1989 and 2 points in 1995. Since our measure of field is based on the doctoral degree, it is possible that the effects of field of employment would be larger. However, since there is relatively little switching across broad fields, this difference is unlikely to be large.

Different Carnegie types of institutions have substantially different rates of pay. For example, in 1995 academics in the elite Research I universities were making 5 percent more than those in Research II universities, 15 percent more than in Doctoral universities, 20 percent more than in Master&rsquos, and 33 percent more than in Baccalaureate institutions. As shown in Chapter 6, women are more likely to be employed in those institutions with lower median salaries. Figure 7&ndash16 shows that adding controls for Carnegie type to the regression containing professional age and field does not substantially reduce the overall gender differences in salary. However, if we examine the gender difference within each type of institution, we find some important differences. Figure 7&ndash17 plots the percentage higher salaries for men by Carnegie type of employer based on the regressions described above. The plot is computed for an academic 15 years from the Ph.D. who is average on other characteristics. The results show that gender differences in salaries have declined since 1973 in all types of institutions, but that the largest changes since 1973 are found in

FIGURE 7&ndash17 Gender differences in salaries by Carnegie type of institution and year of survey. NOTE: Predictions are based on regression estimates.

those institutions with doctoral degree programs. This finding is explored further in the next section where we focus on academics located in Research I universities.

Adding controls for the type of work activity further reduces gender differences in salary, but by a relatively small amount. If we further refine work activity to include distinctions among faculty ranks, shown by the last set of bars in Panel A in Figure 7&ndash16, the overall salary differences between men and women are reduced to less than 8 percent in all years and just 5 percent in 1995.

Tenure Track Faculty at Research Universities

Our findings above have shown that a great deal of the overall gender differences in salaries can be accounted for by the differing professional ages of men and women in academia, with smaller reductions introduced by controls for field, type of institution, and work activity. This section provides a more detailed analysis of faculty with tenure-track positions in research universities (i.e., Research I or Research II universities according to the Carnegie classification). We limit our analyses to this group of scientists and engineers for two reasons. First, work environments differ widely among types of academic institutions. Consequently, the effects of variables such as rank and productivity may operate differently at different types of institutions. By restricting our analyses to a more homo-

genous group of academics, the meaning of our findings should be clearer. Second, tenure-track positions at research universities are often considered to be the most prestigious academic appointments and these faculty train the largest number of Ph.D.s and produce the majority of research in the United States. Accordingly, it is appropriate to give more detailed consideration to this group of academics.

From 1979 to 1995, 6 the overall salary advantage for tenure-track men in research universities dropped slowly from 30 percent in 1979 to 26 percent in 1989 and finally 25 percent in 1995. Note that salary differences in these positions were greater than in the population as a whole. A possible explanation for the slow progress in overall salaries for female faculty in research universities is the greater professional experience of male faculty. Not only is the average male faculty member older, but some research has suggested that the salary advantage for men increases with age. Using data from 1970, Johnson and Stafford (1979) found that the salary disadvantage for women starts small but rises dramatically over time. They conclude (Johnson and Stafford 1979:241): &ldquoAs time passes, the earnings differential between the sexes grows, and this can be attributed to cumulative effects of discrimination or to the market&rsquos reaction to voluntary choices for reduced hours of work and on the job training by women.&rdquo More recently, faculty salary data from the American Association of University Professors show a salary gap between women and men at each rank and across all academic fields, with the widest gap among full professors who tend to be the oldest Ph.D.s (Magner 1996b).

Our data, shown in Figure 7&ndash18, show a more complicated picture. In 1979 (shown by the solid line), there was an increase in the salary advantage for men during years 1 through 5, a nearly constant 6 percent difference from year 5 till year 15, followed by increasing differences until a decline beginning in year 18 (which is based on a small number of female faculty). In 1995 there are larger differences in most years, with a gap of 14 percent in year 1, dropping to 11 percent in year 5. The remaining years track closely with the results from 1979. An alternative way to examine salary differences over the career is to examine gender differences by academic rank. Figure 7&ndash19 shows the percentage higher salaries for men by academic rank for the years 1979, 1989, and 1995. As with years of experience, after controlling for rank women are increasingly less well paid than men later in the career with no evidence of improvement by 1995. Keep in mind that we have not yet controlled for other variables.

Barbezat (1988) reviews the debate on whether rank should be in-

Data are not available for 1973 since information on tenure track status was not collected that year.

FIGURE 7&ndash18 Percent higher salaries for tenure-track men by years since the Ph.D., by year of survey. NOTE: Each year is computed as a 5-year moving average.

FIGURE 7&ndash19 Percent higher salaries for tenure-track men, by rank and year of survey. NOTE: Data are not available for 1973 since information on tenure-track status was not collected that year.

cluded in regressions predicting salary in academic positions. The argument (Hoffman 1976) is that since women may be discriminated against by slower advancement in rank, estimates of discrimination in salary that include rank may be downwardly biased. Ahern and Scott (1981) found that both academic rank and salary are explained by the same set of individual-level variables and did not use rank to predict salary because &ldquorank itself is influenced by gender.&rdquo Nonetheless, we believe that there are important reasons to examine salary differences within rank. It is important to know if men and women in the same rank receive comparable salaries. If, in fact, women are promoted more slowly, the allocation of raises on a percentage basis would make their salaries higher than men within a given rank (since women have been in rank longer), thus providing a lower bound for gender differences independent of the process of rank advancement. Accordingly, in the regression results that follow, rank is included as a predictor of salary.

Figure 7&ndash20 summarizes the most important results of our regression analyses of faculty salaries in research universities. The first set of bars for each survey year shows the predicted percent difference in salaries for men and women after controlling for rank and professional age. The results are similar to those presented earlier, showing that controlling for professional age is largely equivalent to controlling for academic rank. The second set of bars for each year adds controls for characteristics of the scientists, including field, prestige of the Ph.D., elapsed time from baccalaureate to Ph.D., whether the employing institution is public or private, the prestige rating of the individual&rsquos department, and whether it is a Research I university. Significantly, this substantially increases the predicted gender differences in salaries, with predicted differences in salary of 12 percent in 1979, 8 percent in 1989, and 10 percent in 1995. Essentially, these results indicate that men have substantially higher salaries than women with very similar educational backgrounds, institution locations, and experience. We have not, however, included controls for productivity.

As argued by Merton (1973 reprinted from 1942), rewards in science should be based on contributions to the body of scientific knowledge. In academia, unlike many locations in industry and government, these contributions are freely published. Johnson and Stafford (1979) argue that lower salaries may be due to lower productivity. Barbezat (1988), however, questioned whether differences in productivity might also be due to discrimination in publications and found that adding publication variables decreased gender differences in salaries. While there is a huge literature on how to measure scientific productivity (see Long 1992, Gray 1993, and the references cited therein for details), our analysis is limited to simple counts of publications obtained from the Institute for Scientific Information (see Chapter 2 for details). For 1979 we used publications

The evolutionary psychology of women's aggression

Evolutionary researchers have identified age, operational sex ratio and high variance in male resources as factors that intensify female competition. These are discussed in relation to escalated intrasexual competition for men and their resources between young women in deprived neighbourhoods. For these women, fighting is not seen as antithetical to cultural conceptions of femininity, and female weakness is disparaged. Nonetheless, even where competitive pressures are high, young women's aggression is less injurious and frequent than young men's. From an evolutionary perspective, I argue that the intensity of female aggression is constrained by the greater centrality of mothers, rather than fathers, to offspring survival. This selection pressure is realized psychologically through a lower threshold for fear among women. Neuropsychological evidence is not yet conclusive but suggests that women show heightened amygdala reactivity to threatening stimuli, may be better able to exert prefrontal cortical control over emotional behaviour and may consciously register fear more strongly via anterior cingulate activity. The impact of testosterone and oxytocin on the neural circuitry of emotion is also considered.

1. Introduction

Before scientists can begin to explain a phenomenon, they need to be able to describe it. In 1981, Sarah Hrdy pointed out that the ‘competitive component in the nature of women remains anecdotal, intuitively sensed, but not confirmed by science’ [1, p. 130]. Happily, since that time, quantitative analyses and qualitative descriptions of women's aggression have been published. I begin by outlining what these studies have told us, before considering an evolutionary-informed account of the psychological basis of sex (and individual) differences in aggression. Following this, I review whether such a proposal is supported by neuropsychological studies. This ambitious interdisciplinary trajectory takes us from sociology, through psychology, to neuropsychology and endocrinology.

2. The shape of young women's violence

In the United States, girls account for 33% of arrests for simple assault and 24% of aggravated assaults [2]. Despite a 24% increase in female arrests for simple assault between 1996 and 2005, victimization and self-report data indicate that this reflects changes in police practice rather than girls’ behaviour. The male-to-female ratio for assault has remained remarkably stable over time. The gender gap is considerably greater for aggravated than simple assault, reflecting girls’ less injurious behaviour and their lower likelihood of using weapons. Surveys indicate that in the previous year, 40.5% of boys and 25.1% of girls had been in a physical fight [3]. In the previous month, 60% of girls had called another girl names, 50% had sworn at them and 35% had pushed or shoved them [4]. Boys and girls predominantly engage in same-sex aggression, although girls are more likely than boys to target members of the opposite sex. Here, I will focus specifically on same-sex aggression by young women.

The media depiction of girls’ aggression—as an anomalous violation of the feminine gender role—ignores the way that femininity is constructed differently in different cultural contexts. Female aggression is more prevalent in disorganized neighbourhoods with high levels of poverty and low social cohesion [5]. For families living in these neighbourhoods, the frequent absence of a consistent father figure means that mothers (and grandmothers) play a pivotal role. They are strong figures who must cope alone with daily stresses of subsistence living. Many mothers are themselves involved in fighting, especially in defence of their family's good name. Some become actively involved in their daughters’ fights also and, in doing so, become role models and allies [6]. Even when they do not go this far, mothers’ concern for their daughters’ welfare translates into tolerance (and sometimes encouragement) of fighting. Most mothers acknowledge that a girl needs to be able to ‘stand her ground’ and ‘hold her own’. The strength and resilience of women (both mothers and daughters) is not seen as incongruent with femininity: indeed passivity is viewed as a weakness rather than an asset. As Irwin & Adler [7, p. 319] noted, ‘Given the emphasis on female strength, girls lost respect for and even targeted other girls who fell short in fulfilling idealized notions of feminine resilience circulating in the local communities’.

If weakness makes a girl a target, an important benefit of willingness to fight is the avoidance of victimization. Girls’ reports of their fights present aggression as a form of self-defence by emphasizing that their opponents ‘started it’. In some cases, ‘starting it’ refers to a physical assault but more often to verbal taunts to which physical aggression is seen as the appropriate response. The slippery divide between physical and verbal provocation is mirrored in the equally fuzzy distinction between self-defence and reputation enhancement. For many girls, success in a public fight achieves more than the immediate goal of causing an opponent to back off: it promotes a ‘crazy’ or ‘mean’ reputation that will deter others from future attacks [6,8–11]. Reputation enhancement involves a disproportionate response to any perceived act (or rumour) of ‘disrespect’ including staring, failing to move out of the way, behind-the-back gossip and an offensive demeanour that presumes social superiority (a girl who ‘thinks she's all that’). Once established, reputations must be defended against others who are seeking to enhance their own. Girls who start fights are ‘ … trying to make their name. They'll go fight somebody so somebody can be like ‘so-and-so’ fought her, just so their name will be known’ [10, p. 53]. One response to such reputation-seeking challengers is for tough girls to get their retaliation in first. In this way, a self-reinforcing loop develops between self-defence, reputation enhancement, sensitivity to challenge and pre-emptive aggression. But these overarching principles of deterring disrespect and maintaining a reputation obscure the specific triggers that provoke fights. What accusations or provocations constitute acts of ‘disrespect’ worthy of a violent response?

Although girls will fight out of loyalty to family and friends, the ethnographic literature leaves little doubt as to the central role played by boys. Romantic rivalry is one cause. Girls understand their own value in terms of the quality of boys they can attract: ‘Say one guy is good looking, we're all in a fight over who's getting who … If all the girls are fighting for this one really popular guy and one girl gets him, everyone will think she's more popular too’ [12, p. 148]. Once a boyfriend is secured, the relationship must be protected from takeover by other girls: jealousy is another major cause of female fights. When a girl spends too much time with another girl's boyfriend, the anger is firmly targeted at the female interloper rather than at the male partner. This is all the more remarkable because many young men (‘playas’) enjoy trading off girls against one another: ‘He was being with both of ‘em at the same time, and they ended up fighting over him or whatever. In the end, they found out that both of ‘em was getting played by him’ [10, p. 55]. Commentators have noted that jealousy-motivated fights may not be entirely about the boy but about the kudos that a relationship with a high-status boy can bring [12]. At other times, the dividing line between defending a relationship and maintaining a reputation becomes blurred: ‘I don't care about the guy or anything but I'm gonna mess that girl up cause she deserves it. The bitch just be asking for it. The way I see it, I ain't fighting over the boy. I'm fighting the girl because she be acting in a way that says she thinks I'm a punk’ [13, p. 84]. Jealousy can be even more extreme when financial incentives are added, such as when the wronged girl is the mother of her boyfriend's baby [13].

Attractive girls are both the strongest rivals for male attention and the greatest threats to an ongoing relationship. However, it is the combination of attractiveness together with a self-confident awareness of it that seems particularly provocative. Girls who advertise their attractiveness through dress, make-up or demeanour are often targeted [10]. These girls offend on two fronts: they attract more than their fair share of boys and they communicate their felt superiority over other girls. This becomes a form of ‘disrespect’ which adds to the rivalry. While disrespect is often synonymous with status challenge among young men, the same is not true for girls. Girls do not show the hierarchical structure typical of boys’ groups [14]. Girls chiefly want to fit in rather than stand out and it is this which explains the paradoxical finding that girls who are nominated as ‘popular’ (visible, charismatic) are not well liked as friends [15]. Girls who communicate their attractiveness too confidently are targeted not just because they are conspicuous to boys but because they set themselves apart from other girls. This refusal to blend in means that those girls who disdain concern with their appearance or with securing a boyfriend can also be picked on: an inherent sense of superiority is read into their non-conformity [7,10].

Perhaps the strongest evidence that boys lie at the heart of female competition is the terms used to insult others. The same epithets appear frequently in accounts of girls’ fights: ‘slag’, ‘slut’, ‘whore (ho)’ and ‘tart’ [7,10,12,13,16]. The second most common insults are about a girls’ appearance (‘ugly’, ‘fat’). Whether it is delivered directly to an opponent's face or reaches her via gossip and rumour, these terms are often the immediate trigger to physical confrontation.

Sociologists have interpreted the prominence that girls accord to their appearance, boyfriends and sexual reputation as reflecting male hegemony and girls’ internalization of men's sexual objectification of women [12,17]. They argue that girls come to view themselves through the ‘male gaze’, evaluating their worth in terms of boys’ approval and respect. But this obscures a more fundamental issue: why is male approval so important to teenage girls? Why are terms that impugn their sexual reputation so effective at triggering fights? An evolutionary approach goes beyond chastizing girls for their ‘false consciousness’.

3. Moderators of young women's mate competition: an evolutionary perspective

Male aggression (and the paucity of female aggression) has been explained in terms of the greater male variance in reproductive success contingent on polygyny [18]. However, recent developments in evolutionary biology have queried the simplicity of the traditional view of sexual selection which highlights intense male (but not female) competition for mates [19,20]. Rates of female competition are higher in species (like our own) with biparental care and diminished sexual dimorphism. Attempts to trace the evolution of biparental care have used estimates of increased infant cranial size (leading to earlier births, protracted offspring dependence and greater maternal need for assistance) and dated it to 1.5–2 Ma [21]. In terms of sexual dimorphism, archaeological evidence suggests that the relatively modest difference in skeletal size between men and women has remained stable over about 2 Myr and possibly longer [22]. The long history of human biparental care is mirrored in the fact that the vast majority of the world's population live monogamously, despite the large number of societies that permit polygyny. The consequences of monogamy for women have been underappreciated. When a man commits himself to a single woman, his criteria for mate choice shift dramatically upwards [23]. Monogamy entails two-way sexual selection: women as well as men must compete to attain the best possible mates.

While men and women share a preference for mates who are intelligent and kind, there are some traits that assume a higher priority for one sex than the other [24,25]. Women value resources, ambition and generosity which reflect their need for material and emotional support in raising children. Men value youth, attractiveness and fidelity which reflect preference for high reproductive value and the avoidance of cuckoldry. When women compete for well-resourced men, their intersexual competition will entail advertising those qualities that men value and their intrasexual competition will entail discrediting such traits in their rivals. When viewed from this perspective, girls’ preoccupation with enhancing their appearance and defending their sexual reputation becomes more comprehensible, as does the provocative power of accusations of sexual availability and ugliness.

But, as recent theorists have pointed out [26], the severity of mate competition in both sexes is dependent on a range of individual and ecological moderators. Factors such as adult sex ratio, sex-biased mortality, population density and variation in mate quality can impact strongly on the degree of intrasexual competition. Below, I consider how such factors can moderate the intensity of women's aggression.

It is no surprise that age is a strong predictor of female aggression. For both sexes, the teenage years signal entry into the mating arena and a concomitant increase in aggression that is visible in criminal statistics. In line with girls’ earlier sexual maturity, their offending peak occurs 2 years earlier than boys [27]. Allied to this, early menarche is predictive of girls’ aggression. Life-history theory forms the basis for expecting that age of menarche should be responsive to cues from the local environment that canalize development toward a ‘fast’ or ‘slow’ reproductive tempo. These cues have been variously identified as resource scarcity, high rates of early mortality, psychosocial stress, low-quality parental investment, father absence and stepfather presence [28]. By signalling environmental uncertainty and unpredictability, these variables are thought to accelerate pubertal timing and reproduction in an adaptive fashion. In deprived neighbourhoods, girls may experience many of these risk factors simultaneously. These girls begin their sexual careers earlier, putting them at a significant advantage over their peers. In addition, older girls are acutely sensitive to the entry of younger competitors into the mating arena and this may increase their likelihood of victimization and retaliation. Girls who reach menarche early are more likely to be involved in delinquent and aggressive behaviour, and this is especially true for maltreated girls [29] and those living in disadvantaged neighbourhoods [30,31].

The sex ratio in the local neighbourhood determines the intensity of mate competition that a girl faces and in a number of urban centres in the United States this can be markedly skewed. In 2000, the male–female ratio in New York was 90 : 100 and in Philadelphia, 86.8 : 100 [32]. The mortality rate among men is considerably higher than among women especially between the ages of 15 and 35 [33]. At the age of 25, men are three times more likely to die from all causes than women and this rises to a four times greater mortality rate for deaths from external causes. This effect is conditioned by social class and educational achievement so that in poverty-level neighbourhoods, the sex ratio imbalance is especially marked. In addition to mortality, imprisonment also removes a substantial portion of men from the mate pool.

In addition to a paucity of men, there is also considerable variance in male resources. Ambitious and able men leave the neighbourhood as they acquire education and professional employment. Of those that stay, some are destined to unemployment and reliance on welfare (their own or that of their ‘baby mother’). Unable to effectively contribute to the household, such men congregate on stoops and street corners where alcohol and drug abuse is common. At the other end of the income spectrum are ‘flossers’: drug dealers and other members of the underworld economy who are conspicuous spenders and ‘high rollers’ [10]. Their earn-and-burn lifestyle may not be long lasting but their resources make them attractive to many women. ‘Dope guys is straight if they think you ain't dissing them … I date whoever is treating your girl the right way. Me, if a guy got some paper well, it's okay with me. I like fellas that's rolling, least they making it’ [34, p. 130]. The paucity of resource-rich alternatives means that these men are able to impose their preferred short-term mating strategy on local women. This may be far from ideal from young women's point of view but market forces mean that such men often get their way, with young women adapting their resource-extraction tactics accordingly. ‘I tell her take all his paper, all of it, 'cause it's just a matter of time and he's gonna do some rotten dog shit on her … Got to get it when you can. You never know when it's gonna stop and you better get much as you can while you can … When fellas get tired of your pussy, it's good-bye girl, naw, it's get the fuck out of my life bitch! Next bitch!’ [23, pp. 97, 131]. Young women in these neighbourhoods compete for access to men who can supply lavish (if short-lived) resources and whose consumer lifestyle contrasts markedly with the hand-to-mouth existence of the unemployed. Among middle-class women, male resource variance is much less extreme, parental support is available, and consequently the need to resort to physical aggression is less: female competition is generally restricted to intersexual rivalry for male attention.

In impoverished neighbourhoods, the willingness of some women to trade sex for resources makes verbal assaults on a woman's sexual reputation particularly apt and creates fear among others that they will tarred with the same brush. But everywhere (regardless of social class, race or ethnicity) accusations of promiscuity are powerful forms of verbal attack because they jeopardise a young woman's chance of finding a reliable long-term mate. The sexual revolution has done little to diminish men's reluctance to marry ‘easy’ women. In addition to damaging a rival's reputation, women as a sex benefit by using verbal attacks to control other woman's sexuality [35]. Because sex is a resource that men want and women can supply, women gain by maintaining a high ‘market price’ for sex. By making sex contingent on commitment, women encourage men to pursue a more monogamous strategy. Women who dispense sex too cheaply reduce the bargaining power of other women. In underclass neighbourhoods, the intense competition and the paucity of men who are willing to commit increases the temptation to offer sex at a low level of male investment. The term ‘whore’ is used not only to tarnish a rival in men's eyes but also to mark her out as someone who has selfishly sold other women out.

4. Psychological mediators of sex differences in aggression

Close description of the ecological setting, culture and dynamics of young women's fighting is illuminating, but it should not distract us from the fact that, everywhere and at every historical period, physical aggression between women is less frequent and less severe than between young men. As the dangerousness of the aggressive act increases so does the magnitude of the sex difference. Just as the sex difference is greater for aggravated than simple assault, so same-sex homicides show the greatest imbalance with 97% committed by men around the world [36]. Meta-analysis of self-report studies find effect sizes of d = 0.59 for physical aggression, d = 0.19 for verbal aggression and d = 0.05 for indirect aggression [37]. In other primate species also, aggression between males is more injurious than between females [38].

This pattern of sex differences has been explained in terms of sexual selection [37]. Daly & Wilson [18] focused on the greater variance in male reproductive success which offers incentives for intrasexual aggression in the quest for dominance and resources. While not denying the existence of female aggression, they emphasized its relative paucity which they explained in terms of the absence of reproductive incentives: females in polygynous species do not need to compete for copulations. My approach [39,40] focuses on the greater costs attendant on females’ aggression. It does not assume a polygynous mating system and recognizes that incentives other than copulation opportunities can support female aggression.

For both sexes, reproductive success is measured in the number of offspring who survive to adulthood and who themselves reproduce. Given that maternal investment exceeds paternal investment, this burden is disproportionately borne by females. Following gestation, mothers expend calories in lactation and in feeding young children who must also be monitored and defended from natural accidents and attack by conspecifics. The advantages that high rank could confer in accomplishing these tasks make it all the more surprising that females do not engage in dominance contests to the same extent as males. In many species, dominant females have priority of access to food, supplant others from feeding sites and are less subjected to predation. They can suppress reproduction in subordinates, have shorter interbirth intervals and produce more surviving offspring [41].

Among primates, dominance relations are most discernible in female-bonded species (where daughters remain in their natal group). Dominance in such groups is inherited through matrilines rather than achieved by aggression. Matrilines can be ranked with respect to one another with virtually no overlap. There are three rules that predict a female's rank [42]. First, daughters inherit their mother's rank on her death without requirement for direct combat. Second, mothers dominate daughters for life. Third, in adulthood, younger sisters dominate older sisters. This may be a maternal strategy to ensure that a younger daughter cannot improve her rank by forming a coalition with her sister [43]. Matrilineal inheritance creates a remarkably stable system. In baboons, a juvenile female's rank at birth correctly predicted her adult rank in 97% of cases [44]. In female gelada baboons, there was no alteration in rank position over a 4-year period despite the disruptive potential of births, deaths, fissions and male takeovers [45]. Among capuchins, the female hierarchy was stable over 22 years [46]. Studies which have experimentally manipulated female groupings demonstrate that challenge for dominance is rare and occurs only when the odds are strongly in favour of the challenger, referred to as a ‘minimal risk’ strategy [42]. By contrast, in non-female bonded primates, for instance chimpanzees (in which females transfer to a new group at adolescence), dominance relations are hard to detect and rarely involve outright aggressive confrontation [47]. In bonobos also, female relationships are egalitarian and aggression between females constituted only nine out of 325 aggressive episodes recorded [48].

The advantages of dominance combined with the reluctance of females to risk aggression in its pursuit suggest that there must be associated costs. Aggression involves the possibility of injury and death and their consequences on reproductive success are not equal for men and women. For women, with their limited variance in fecundity, child survival plays a critical role in their ultimate reproductive success. A review of 45 studies of natural fertility populations with limited access to medical care indicated that a mother's death has uniformly detrimental effects on her children's chances of survival [49]. The effect is strongest in the early years of a child's life. Pavard et al. [50] conducted a careful study of births in seventeenth- and eighteenth-century Quebec. They excluded cases in which the baby died immediately following birth (to exclude obstetric complications and cross-infection) and corrected for between-family heterogeneity (intrinsic family mortality levels). A mother's death during the neonatal period increased the odds of her child dying in the postnatal period (28–299 days) by 5.52. Although the effect was less extreme at later ages, the death of a mother in early childhood (3–5 years) increased the odds of her child dying in the same period by 2.48 and by 1.45 if her death occurred during late childhood (5–15 years).

A contrast with the effect of paternal death is instructive. In every study in which there was a direct comparison of the effect of maternal and paternal deaths, the loss of a father had substantially less impact [49]. Indeed in 15 out of 22 (68%) studies, the presence of a father had no impact at all on child survival. Although it is commonly assumed that fathers are important in provisioning, paternal death had no effect on child survival among the South American Hiwi who live in nuclear families in which fathers contribute meat and direct child care. (This is not to deny the contemporary evidence that fathers improve their children's educational and social life chances [51]. My concern here is with the centrality of the mother during human evolution.) It appears that paternal care is facultative rather than obligatory in our species and that a father's death can be compensated for by help from grandmothers (especially maternal grandmothers) and older siblings.

Infancy and early childhood are vulnerable periods. Among the Ache of Paraguay, 13% of children die before the end of their first year and 27% before the age of five [52]. The mother is the infant's most important line of protection from starvation, attack and accidents. Given the short interbirth interval in our species, women would have been caring for a vulnerable infant and/or pregnant for a substantial proportion of their reproductive career [53]. A woman's reproductive success may have depended on the avoidance of risky behaviours, including aggression.

This raises the question of the psychological adaptation that mediates women's greater avoidance of risky confrontations. Aggression can be conceived of as a trade-off between anger (approach) and fear (avoidance) which suggests that alterations in the intensity of these fundamental affective responses may underlie willingness to aggress. The possibility that women's lower level of anger might explain their greater desistance is not supported by research. Meta-analyses indicate no sex difference in anger either in adults [37] or in children [54]. In addition, a raised threshold for anger might protect women from aggressive confrontations but not from other risky forms of behaviour. Yet, there is ample evidence that women are more risk averse than men [55].

By contrast, there is a considerable body of work suggesting that women are more fearful than men. This difference is visible in childhood [54] and international surveys have found significant sex differences in the reported intensity and duration of fear in adults [56]. Women and girls show more corrugator muscle and electrodermal activity than men when viewing negative images and a stronger startle response to a noise blast delivered during exposure to fear-inducing pictures [57]. Cross-culturally, women exceed men on trait neuroticism [58] and are more prone to phobic fears and anxiety [59]. Under conditions of threat, women judge the danger to be greater than men do [60]. Women orient away from (rather than toward) threat and with greater intensity then men do [61]. Following the tragedy of the World Trade Center, a nationally representative sample of Americans participated in a survey which included assessments of fear and anger [62]. Women reported significantly higher levels of fear and gave higher risk estimates than men did. Some research suggests that fear has stronger aggression–suppressing effects on women than men [63]. After being subjected to stressors which both sexes rated as inducing fear, women in the high-stressor condition subsequently gave lower intensity shocks while, among men, stressor intensity was not related to shock delivery. Two meta-analyses have concluded that the magnitude of sex differences found in laboratory studies of aggression is positively correlated with the sex difference in ratings of participants’ personal danger [64,65].

This emphasis on fear as a key factor in explaining sex differences carries implications for individual differences among young women. In deprived and dangerous neighbourhoods, girls frequently note the need to suppress expression of fear in order to avoid victimization. This theme echoes through much of the qualitative work on female violence, from Philadelphia (‘If I seem like I'm scared to fight, some girl is gonna think she can mess with me all the time’ [6, p. 38]) to Glasgow (‘ … ‘Cos if you show fear of somebody they're just gonna walk all over the top of you. If you show fear of them, they always come back tae you’ [66, p. 130]). Among young people with high exposure to violence in their communities, reduced levels of fear (reflected in lower heart rate) are associated specifically with proactive (unprovoked) forms of aggression [67]. This mirrors the narratives of aggressive girls who describe the importance of fearlessness and the use of pre-emptive aggression in the development of a fierce reputation.

5. Neuropsychology of sex differences in emotion

We now turn to the question of whether we are yet able to identify neuropsychological, hormonal and physiological correlates of sex differences in aggression-related emotion. Before doing so, it is important to bear in mind the visual stimuli that are used to induce emotions in neuroimaging studies. Of special relevance to understanding sex differences in aggression are responses to ‘threatening’ stimuli. These are chiefly facial expressions of fear and/or anger and, less frequently, aggressive stimuli such as weapons.

(a) Amygdala

I have noted the considerable behavioural evidence that women are more fearful than men. Indeed the twofold greater prevalence of anxiety disorders among women has been the impetus to many imaging studies looking for neural correlates of this sex difference. The chief focus of such studies has been the amygdala. The amygdala is an almond-shaped subcortical structure (composed of more than 10 nuclei) in the temporal lobe. For many years, it was believed that the amygdala was uniquely associated with fear responses, although it is now thought to register other strong or salient stimuli. Afferent sensory inputs to the lateral nucleus of the amygdala are coordinated with efferent outputs from the central nucleus which control behavioural, autonomic and endocrine fear responses. We would expect to see a stronger amygdala response to threat in women reflecting their greater fearfulness.

Meta-analyses conclude that women do show greater activation to threat in the limbic system, especially the amygdala ([68,69], but see [70]). In one study, women showed a greater extent (rather than magnitude) of activation together with a more extended time course: during exposure to threatening stimuli, women exhibited sustained amygdala activation (and skin conductance), whereas men's response diminished more quickly [71]. This suggests that women may register external threat more strongly and more persistently than men. However, because the majority of neuroimaging studies use participants of only one sex, meta-analytic conclusions are based on comparisons of neural responses in men and women to different stimuli [68].

Although amygdala activation has been chiefly implicated in fear, it has also been linked to aggression. Despite experimental bracketing of fearful and angry faces as representing ‘threat’, there is evidence that perception of these two stimuli may activate different brain regions. While fearful faces reliably activate the amygdala associated with avoidance, angry faces preferentially (or additionally) activate oribitofrontal areas implicated in emotional control [72,73]. Studies that include men and women in the same study are important in establishing whether there are sex differences in brain activation to identical ‘threatening’ stimuli. McClure et al. [74] compared men's and women's reactivity to angry and fearful faces. The relative engagement of the amygdala bilaterally to angry faces was greater in women suggesting that women react more fearfully than men to unambiguously threatening (angry) faces. Relative to baseline fixation, women showed significantly greater activation than men over the whole ‘fear circuit’ (amygdala, orbitofrontal and anterior cingulate cortex (ACC)) to angry but not fearful faces. By contrast, men showed a less specific pattern of increased orbitofrontal (but not amygdala) activation to both stimuli. Men's reactivity to angry faces varies as a function of trait anxiety and anger [75]. In men, but not women, heightened amygdala reactivity is associated with a combination of high anxiety and high reactive anger. There is then some support for the proposal that amygdala activation may be more closely associated with fearful responses to threat in women and (fear-related) reactive anger in men.

As with other regions that are sexually dimorphic in size, the amygdala contains a high concentration of sex hormone receptors. Because testosterone (T) has been linked to aggression, we might expect to see T-linked differences in neural response. Depending on whether amygdala activity is viewed as reflecting fear or anger, different predictions follow. From the fear viewpoint, T has anxiolytic effects suggesting that endogenous T levels should reduce amygdala reactivity to threat, as has been found in men but not in women [76]. The amygdala also controls automatic responses to threat: T administration to young women reduced attention to fearful faces [77], skin conductance during viewing of negative pictures [78] and the magnitude of fear-potentiated startle response [79]. On the other hand, some have assumed that amygdala activity reflects anger rather than fear [80]. If so, we would expect to see a positive association between T and amygdala activity in response specifically to angry faces (since fearful faces are less likely to elicit anger). Although in both sexes, higher levels of endogenous T are associated with a stronger amygdala response to ‘threatening’ stimuli [81], many studies do not analyse fear and anger stimuli separately [82,83]. In one study that did, young men's amygdala reactivity did not differ significantly to angry versus fearful faces and their endogenous T levels were equally correlated with their amygdala responses to both stimuli [84]. However, administration of T to young women increases amygdala reactivity to angry faces [85]. Despite these inconclusive results, many researchers interpret the pattern of data as indicating that T causes a shift toward a more ‘male typical’ response with enhanced amygdala activity (reflecting anger registration) combined with a reduction of fear both physiologically (skin conductance) and behaviourally (startle response).

The neuropeptide oxytocin (OT) is widely recognized for its anxiolytic properties associated with enhanced trust and cooperation [86]. While OT administration reduces amygdala reactivity to threat in men, it has the opposite effect in women [87,88]. The full implications of this finding have yet to be appreciated and underscore the importance of studying both sexes in relation to hormonal effects. Despite the disproportionate use of male participants in OT studies, OT is thought to be particularly relevant to women because oestrogen stimulates OT release, and promotes OT receptor gene expression and OT binding in the amygdala. Given women's stronger fear response to threat, exogenously administered (and by implication endogenously synthesized) OT has been interpreted as enhancing the female fear response as an adaptation for maternal survival and infant protection [88]. The fact that OT enhances, rather than diminishes, attention to potential threat in the environment casts doubt on the popular ‘tend-and-befriend’ hypothesis which is based on the presumed anxiolytic effect of OT [89]. In contrast to men's fight-or-flight response to threat, this hypothesis proposed that OT-mediated stress reduction enabled women to remain calm, blend into the environment and bond with their infants and with other females.

Comparing T and OT studies, we see that the interpretation of results is often selective. Studies which administer OT interpret enhanced amygdala activity as reflecting fear and avoidance, whereas T administration studies interpret the same effect as enhanced anger and approach. With respect to both hormones, we should consider the possibility that the effects of exogenous hormones on male and female brains are likely to differ. Given the greater OT receptor density in the female brain, administration of OT may result in very high levels of uptake and dosage effects may be nonlinear, as has been found with other hormones. It is possible that at least some part of T's neural effects occur via aromatization to oestradiol in presynaptic terminals which in women may enhance sex-typical fear in response to threat. T is likely to produce very different effects on the female brain which, unlike the male brain, has not been prenatally organized by T. Gene expression in the brain is sexually dimorphic and controlled by sex hormones: the same hormone can result in the expression of different genes in male and female brains [90,91].

(b) Amygdala–frontal connectivity

Lower-level affective tendencies to approach or avoid stimuli located in the limbic system are part of a ‘reflexive’ behavioural control system sculpted chiefly by evolutionary forces. In humans, these tendencies are subject to higher level ‘reflective’ control. Emotional intensity and behavioural response can be modulated by the prefrontal cortex, especially the orbitofrontal (OFC) region, which has direct connections to the amygdala. In neuroimaging studies, negative correlations are found between amygdala and OFC activity in impulsively aggressive individuals [92]. In studies in which participants are instructed to imagine aggressing against [93] or harming [94] another person, deactivation of the OFC has been found. Given the modulatory role of the prefrontal cortex (PFC), studies have looked for sex differences in these regions. Women have a larger ventromedial PFC and right lateral OFC [95,96]. A meta-analysis of 88 studies reported greater OFC activity in women to facial stimuli depicting negative emotion ([68], see also [69]). This suggests that women may be more efficient in spontaneously regulating emotional responses.

This is supported by studies of hormones and the neurotransmitter serotonin. While progesterone increases functional connectivity between the amygdala and PFC [97], T reduces it, while leaving connectivity to the brain stem unaffected [82,98]. OT, a neuropeptide upregulated by oestrogen, appears to have opposite effects to those of T. OT enhances amygdala–prefrontal connectivity [99] while reducing amygdala coupling with the brain stem [100].

Serotonin (5-HT) plays a key role in the functional connectivity between the PFC and the amygdala. There is a dense concentration of 5-HT receptors in the limbic system (including the amygdala) with projections to the prefrontal cortex. Dietary tryptophan depletion (which reduces 5-HT levels) reduces connectivity in the prefrontal–amygdala circuitry specifically when viewing angry faces [101]. Women have higher 5-HT transporter availability and, because this regulates 5-HT neurotransmission, baseline serotonin may be higher in women than men. Studies have reported a higher density of 5-HT1A receptors in women in areas including the amygdala and medial and orbital PFC [102]. Receptor density in these areas is significantly negatively correlated with lifetime aggression. In animal research, 5-HT receptor density is also negatively correlated with T. Although this has not been replicated with humans, men (but not women) with high levels of aggression are characterized by a combination of high T and low 5-HT [103]. Reduced serotonin availability or uptake, associated with high T, may explain men's diminished prefrontal control over emotion-driven behaviour.

(c) Amygdala–peripheral connections

The central amygdala projects downward to the hypothalamus and brain stem to initiate autonomic and hypothalamic–pituitary–adrenal (HPA) responses to threat. Sex differences in self-reported and behavioural measures of fear are not matched by differences in sympathetic nervous system reactivity. When fear is induced through incremental behavioural approach to spiders [104], inhalation of CO2-enriched air [105], affective images [106], scary movie clips [107] or emotional imagery [108], sex differences in heart rate and blood pressure are not found. In the HPA system, evidence indicates somewhat higher salivary cortisol measures in men after experimental stress induction [109].

(d) Anterior cingulate and anterior insula cortices

It has been suggested that this anomaly—higher self-reported fear in women combined with an absence of sex differences in physiological reactivity—might be resolved by sex differences in the conscious experience of emotion. Two structures which are often jointly activated have been implicated: the anterior insula cortex (AIC) and the anterior cingulate cortex (ACC). These structures monitor bodily states (including thirst, touch and sexual arousal) and are also activated in response to a wide range of emotions, including fear and anger. Their co-activation makes it difficult to tease out their respective contributions to emotional states, but it has been proposed that the AIC monitors the internal neural and visceral state (interoception) and the ACC mediates the subjective experience of emotion [110,111].

Women have greater grey matter volume and higher resting-state blood flow to the ACC. They show stronger ACC (as well as amygdala) activity than men in an electric shock conditioning paradigm, despite no sex difference in autonomic system reactivity [112]. A meta-analysis of 65 studies examining sex differences in neural activation to emotional stimuli found that women showed greater density of activation in the ACC [68] and men in the AIC [113]. In response to specifically negative stimuli, women showed greater reactivity than men in the ACC suggesting that women process stimuli in terms of subjective emotional state. Women but not men when asked to imagine acts of aggression show enhanced ACC activity [114]. OT enhances activity in the ACC and increases its connectivity with the amygdala [115,116]. Men respond to negative stimuli with greater activity in the AIC. It has been suggested that this may be because men process emotional information in terms of interoceptive states and implications for action. The possibility that women have a more intense subjective experience of emotion than men goes some way to explaining the paradoxical finding that women's self-reports of the intensity of many emotions is generally higher than men's despite few sex differences in autonomic indices. This is especially true of fear.

Some have suggested that the absence of sex differences in autonomic correlates of fear is explicable by men's reluctance to admit fear because of male gender role proscriptions on acknowledging vulnerable emotions. Although self-reports of fear and anxiety are correlated negatively with masculinity and positively with femininity [59], studies which control for gender role still find a significant effect of biological sex in self-reports [117]. In a behavioural task in which some participants were told that their self-report of fear was verifiable by heart rate monitors, the significant sex difference in fear ratings was unaffected [104]. While social and cultural expectations about gender are important, it appears that they cannot fully explain sex differences in self-reported emotional experience.

To summarize, the available data suggest that women register threat more strongly in the amygdala, although the sexes differ little in their autonomic and HPA responses. Women may have a stronger subjective awareness of fear associated with greater ACC activity. They show a stronger OFC reactivity to negative emotion, have a higher density of serotonin receptors and lower levels of T (which reduces connectivity between the OFC and amygdala) perhaps making them better able to exert control over the behavioural expression of emotion.

6. Conclusion

Like all living organisms, women compete. The real questions concern what they compete about and how lethal their competition is. Among young Western women living in deprived circumstances, aggression often revolves around competition to acquire and retain mates. The same finding has been reported in a cross-cultural survey of the Human Area Relations File [118], as well as data from Zambia [119] and Aboriginal women [120]. Among the Tsimane of Bolivia [121], conflict about men constituted 25% of women's arguments, compared to 28% about social relationships (e.g. defecting on a social exchange) and 19% about food sharing or theft. However, these percentages varied significantly by age. Under the age of 20, men were the leading cause of conflict (Tsimane girls marry much younger than in the West) although this was overtaken by quarrels over mutual social obligations between women in the 30–40 age range. However, as we have seen, physical forms of aggression are most common among younger women. Anthropological research alerts us to the importance of cultural factors in female aggression. Young women's behaviour is shaped by local understanding of the meaning of ‘femininity’ and expectations of appropriate response to challenge. These cultural values in turn are likely to be responsive to ecological factors including sex ratio, poverty and variance in male resources.

The critical role that mothers play in infant survival is now well documented and provides an evolutionary platform for expecting that, despite the benefits of achieving dominant status, there are associated costs. I have emphasized the need to avoid escalated aggression if women are to ensure the survival of infants with their high replacement costs. Others have pointed out that biological ‘masculinization’ associated with intense competition may compromise female fertility and fecundity, limiting the evolution of extreme female aggressiveness [19,41]. In our own species, the psychological evidence points strongly toward greater fear (rather than lower anger) as the proximate mediator of women's less intense aggression. A sex difference in fear also explains women's lower involvement in a range of risky activities such as extreme sports, dangerous driving and criminal activities [55,122]. It converges with considerable evidence of women's greater punishment sensitivity and vulnerability to anxiety and depression [123].

Despite abundant self-report and behavioural evidence of sex differences in fear, neuropsychological research is still in its infancy. Understanding aggression (in both sexes) requires more reliable tools to distinguish between qualitatively different emotional and motivational responses to threat stimuli. A fearful face communicates the possibility of danger at an undetermined place in the environment and may evoke corresponding fear in the viewer. An angry face directed to the viewer more clearly indicates threat but this may evoke either fear or anger (or both). Because fear and anger are associated with amygdala reactivity, there is a tendency for researchers’ interpretation of their results to be driven by their knowledge of sex differences. Hence, heightened amygdala activity is taken to indicate fear in women and anger in men. At present, evidence suggests that women show a stronger amygdala registration of threat, combined with a stronger subjective awareness of emotion and, perhaps, stronger inhibitory prefrontal control. Further studies are needed in which men and women experience the same stimuli and their neural responses are directly compared. Ultimately, these emotional responses must be linked to aggressive behaviour and this is challenging for neuropsychology because a scanner restricts natural movement. However, asking participants to vividly imagine aggression may be the way forward: a number of studies now confirm that neural responses elicited when participants imagine acts or feelings correspond to those seen during the authentic experience [124,125].

From an evolutionary viewpoint, variance between women in reproductive outcomes tells us that women are in competition. The extent to which that competition takes the form of aggressive confrontation varies as a function of ecological pressure. Ethnographic studies can provide important descriptions of the causes, context and culture of female fighting. The challenge for psychology is to identify the psychological and neural mechanisms that underpin its expression and form, and that restrict its severity relative to men's.

How Men's Brains Are Wired Differently than Women's

Men aren't from Mars and women aren't from Venus, but their brains really are wired differently, a new study suggests.

The research, which involved imaging the brains of nearly 1,000 adolescents, found that male brains had more connections within hemispheres, whereas female brains were more connected between hemispheres. The results, which apply to the population as a whole and not individuals, suggest that male brains may be optimized for motor skills, and female brains may be optimized for combining analytical and intuitive thinking.

"On average, men connect front to back [parts of the brain] more strongly than women," whereas "women have stronger connections left to right," said study leader Ragini Verma, an associate professor of radiology at the University of Pennsylvania medical school. But Verma cautioned against making sweeping generalizations about men and women based on the results.

Previous studies have found behavioral differences between men and women. For example, women may have better verbal memory and social cognition, whereas men may have better motor and spatial skills, on average. Brain imaging studies have shown that women have a higher percentage of gray matter, the computational tissue of the brain, while men have a higher percentage of white matter, the connective cables of the brain. But few studies have shown that men's and women's brains are connected differently.

In the study, researchers scanned the brains of 949 young people ages 8 to 22 (428 males and 521 females), using a form of magnetic resonance imaging (MRI) known as diffusion tensor imaging, which maps the diffusion of water molecules within brain tissue. The researchers analyzed the participants as a single group, and as three separate groups split up by age.

As a whole, the young men had stronger connections within cerebral hemispheres while the young women had stronger connections between hemispheres, the study, detailed today (Dec. 2) in the journal Proceedings of the National Academy of Sciences, found. However, the cerebellum, a part of the brain below the cerebrum that plays a role in coordinating muscle movement, showed the opposite pattern, with males having stronger connections between hemispheres.

Roughly speaking, the back of the brain handles perception and the front of the brain handles action the left hemisphere of the brain is the seat of logical thinking, while the right side of the brain begets intuitive thinking. The findings lend support to the view that males may excel at motor skills, while women may be better at integrating analysis and intuitive thinking.

"It is fascinating that we can see some of functional differences in men and women structurally," Verma told LiveScience. However, the results do not apply to individual men and women, she said. "Every individual could have part of both men and women in them," she said, referring to the connectivity patterns her team observed.

When the researchers compared the young people by age group, they saw the most pronounced brain differences among adolescents (13.4 to 17 years old), suggesting the sexes begin to diverge in the teen years. Males and females showed the greatest differences in inter-hemisphere brain connectivity during this time, with females having more connections between hemispheres primarily in the frontal lobe. These differences got smaller with age, with older females showing more widely distributed connections throughout the brain rather than just in the frontal lobe.

Currently, scientists can't quantify how much an individual has male- or female-like patterns of brain connectivity. Another lingering question is whether the structural differences result in differences in brain function, or whether differences in function result in structural changes.

The findings could also help scientists understand why certain diseases, such as autism, are more prevalent in males, Verma said.

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Smashing the patriarchy: why there's nothing natural about male supremacy

F athers are happier, less stressed and less tired than mothers, finds a study from the American Time Use Survey. Not unrelated, surely, is the regular report that mothers do more housework and childcare than fathers, even when both parents work full time. When the primary breadwinner is the mother versus the father, she also shoulders the mental load of family management, being three times more likely to handle and schedule their activities, appointments, holidays and gatherings, organise the family finances and take care of home maintenance, according to Slate, the US website. (Men, incidentally, are twice as likely as women to think household chores are divided equally.) In spite of their outsized contributions, full-time working mothers also feel more guilt than full-time working fathers about the negative impact on their children of working. One argument that is often used to explain the anxiety that working mothers experience is that it – and many other social ills – is the result of men and women not living “as nature intended”. This school of thought suggests that men are naturally the dominant ones, whereas women are naturally homemakers.

But the patriarchy is not the “natural” human state. It is, though, very real, often a question of life or death. At least 126 million women and girls around the world are “missing” due to sex-selective abortions, infanticide or neglect, according to United Nations Population Fund figures. Women in some countries have so little power they are essentially infantilised, unable to travel, drive, even show their faces, without male permission. In Britain, with its equality legislation, two women are killed each week by a male partner, and the violence begins in girlhood: it was reported last month that one in 16 US girls was forced into their first experience of sex. The best-paid jobs are mainly held by men the unpaid labour mainly falls to women. Globally, 82% of ministerial positions are held by men. Whole fields of expertise are predominantly male, such as physical sciences (and women garner less recognition for their contributions – they have received just 2.77% of the Nobel prizes for sciences).

According to a variety of high-profile figures (mainly male, mainly psychologists), bolstered by professorships and no shortage of disciples, there are important biological reasons for why men and women have different roles and status in our society. Steven Pinker, for instance, has argued that men prefer to work with “things”, whereas women prefer to work with “people”. This, he said, explains why more women work in the (low-paid) charity and healthcare sector, rather than getting PhDs in science. According to Pinker, “The occupation that fits best with the ‘people’ end of the continuum is director of a community services organisation. The occupations that fit best with the ‘things’ end are physicist, chemist, mathematician, computer programmer, and biologist.”

Others deny societal sexism even exists, insisting that the gender roles we see are based on cognitive differences – spoiler: men are more intelligent. “The people who hold that our culture is an oppressive patriarchy, they don’t want to admit that the current hierarchy might be predicated on competence,” Jordan Peterson has said, for instance. His reasoning suggests that women would be happier not railing against it but instead observing their traditional gender roles. Such theories have been demolished by a range of scholars, including neuroscientist Gina Rippon and psychologist Cordelia Fine.

There are certainly biological differences between men and women, from their sexual anatomy to hormones. Yet even this isn’t as clear cut as it seems. For instance, around one in 50 people may be “intersex” with some sort of atypical chromosomal or hormonal feature – that’s about the same as the proportion of redheads. Men’s brains are on the whole slightly larger than women’s, and scans reveal some differences in the size and connectedness of specific brain regions, such as the hippocampus, in large samples of men and women.

Illustration: Timo Kuilder

And yet, only a tiny percent (between 0 and 8%) of individual men and women turn out to have a typically “male” or “female” brain. Most people are somewhere in the middle, and whether someone has skills for maths, spatial awareness, leadership or any other gendered attribute can not be predicted from knowing their sex, as multiple studies have shown. Anatomically and cognitively, there are more differences within the two sexes than between them.

There is no evidence that women are any less capable of the jobs and social positions that men predominantly hold. When women are given the opportunity to hold “male” roles, they show themselves to be equally proficient. Researchers recently calculated that it was bias against women, not under-representation, that accounts for the gender distribution seen in the Nobel prizes, for instance. Women are not less intelligent, less logical or less able than men. The roots of patriarchy, in other words, cannot be found in our biology.

Male supremacy, for all its ubiquity, is surprisingly recent. There’s compelling evidence that patriarchal societies date back less than 10,000 years. Humans probably evolved as an egalitarian species and remained that way for hundreds of thousands of years. One clue is in the similar size of human males and females, which show the least disparity of all the apes, indicating that male dominance is not the driving force in our species. In fact, equality between the sexes in our early ancestry would have been evolutionarily beneficial. Parents who were invested in both girls and boys (and the grandchildren from both) gave our ancestors a survival advantage, because this fostered the critical wider-ranging social networks they depended on to exchange resources, genes and cultural knowledge.

Today, hunter-gatherer societies remain remarkable for their gender equality, which is not to say women and men necessarily have the same roles, but there is not the gender-based power imbalance that is almost universal in other societies. In contemporary hunter-gatherer groups, such as the Hadza people of Tanzania, men and women contribute a similar number of calories, and both care for children. They also tend to have equal influence on where their group lives and who they live with.

A Bribri community, in Costa Rica. Photograph: Maxime Bessieres/Alamy Stock Photo/Alamy

Matriarchal societies may also have been more common in our ancestral communities. Strong female relationships would have helped to glue a larger community together, and being able to rely on friends to babysit would have given our ancestors the time and energy to support the group through food provision and other activities. Indeed, there are several societies where matriarchy is the norm – I’ve visited some of them, including the cocoa farming Bribri people of Costa Rica, and the rice farming Minangkabau of Sumatra, Indonesia. These are communities in which women are the landowners and decision makers.

In other words, humans are not genetically programmed for male dominance. It is no more “natural” for us to live in a patriarchy than in a matriarchy or, indeed an egalitarian society. In the same way, it is just as natural for humans to eat a “paleo” diet as it is to eat bubblegum-flavoured candyfloss to have sex as a man and a woman or as three men to live in a straw hut or in a glass bubble beneath the ocean. This is because, unlike other animals, we are cultural beings – for our species, culture is our nature, and key to understanding our behaviours and motivations.

Social, technological and behavioural invention are part of our nature – part of what it means to be human. We are driven by culture more than instinct. And our culture influences our environment and our genes. Our extraordinarily flexible, cumulative culture allows us to make ourselves even as we attribute our successes and failings to our genes.

That’s not to say that just because a cultural trait has emerged it is necessarily “good”. Patriarchal norms, for instance, are damaging to our health and our societies, increasing death and suffering, and limiting humanity’s creative potential. We are, though, neither slaves to our biology nor our social norms – even if it can feel that way.

Human cultural conditioning begins at birth, indeed, social norms even have an impact before birth: one study found that when pregnant women were informed of the sex of the baby they were carrying, they described its movements differently. Women who learned they were carrying a girl typically described the movements as “quiet”, “very gentle, more rolling than kicking” whereas those who knew they were carrying a boy described “very vigorous movements”, “kicks and punches”, “a saga of earthquakes”.

Traditional cow racing, celebrating the end of the harvest by the Minangkabau people. Photograph: Robertus Pudyanto/Getty Images

Many of the ideas we consider universally held are simply the social norms in our own culture. Liberté, égalité, fraternité may be values worth dying for in France, for instance, but personal freedom is not considered important or desirable for other societies, which prioritise values such as purity instead. Consider the idea of responsibility. In my culture, if you deliberately hurt a person or their property this is considered a much worse crime than if you did it by accident, but in other cultures, children and adults are punished according to the outcome of their actions – intentionality is considered impossible to grasp and therefore largely irrelevant.

The biological differences between males and females, or indeed between ethnic groups, tell us nothing about how intelligent, empathetic or successful a person is. Modern humans are 99.9% genetically identical. Although we have expanded far beyond our tropical evolutionary niche over tens of thousands of years, we have not speciated – we have not even diversified into different subspecies. Our ancestors have not needed to make dramatic biological adaptations to the very different environments we live in, because, instead, we culturally evolved and diversified into a complexity of differently adapted cultures, each with their own social norms.

It is our cultural developing bath, not our genes, that profoundly changes the way we think, behave and perceive the world. Studies comparing the neural processing of populations of westerners and East Asians, for example, show that culture shapes how people look at faces (westerners triangulate their gaze over eyes and mouth, whereas East Asians centralise their focus). Language reveals our norms and shapes the way we think. Children who speak Hebrew, a strongly gendered language, know their own gender a year earlier than speakers of non-gendered Finnish. English speakers are better than Japanese speakers at remembering who or what caused an accident, such as breaking a vase. That’s because in English we say “Jimmy broke the vase”, whereas in Japanese, the agent of causality is rarely used they will say: “The vase broke.” The structures that exist in our language profoundly shape how we construct reality – and it turns out that reality, and our human nature, differ dramatically depending on the language we speak. Our brains change and our cognition is rewired according to the cultural input we receive and respond to.

Many of our social norms evolved because they improve survival, through group cohesion, for instance. But social norms can also be harmful. There is no scientific basis for the belief that a person’s skin colour or sex has any bearing on their character or intelligence. However, social norms can affect a person’s behaviour and their biology. Social norms that classify particular groups to the bottom of a social hierarchy encourage society to collude with that positioning and those people do worse in outcomes from wealth to health, strengthening the norm. A major study, by researchers at Berkeley, of 30,000 American shift workers found that black, Hispanic and other minority workers – particularly women – are much more likely to be assigned irregular schedules, and the harmful repercussions of this were felt not just by them but also by their children, who fared worse.

The danger of ascribing genetic and biological bases for our actions is that individuals and groups are not given equal opportunities in life, and they suffer. It is, after all, very convenient to believe that the poor are feckless and undeserving, morally weak or stupid, rather than casualties of a deeply unfair systemic bias. Equally, it’s much more appealing to think of one’s own successes as down to some sort of innate personal brilliance rather than luck and social position.

If we persist in the idea that there is a natural – a best – way to be a human, then we blind ourselves to the great diversity of potential ways of being, thinking and feeling, and impose social limitations on those whose life choices are no less legitimate than ours. It’s worth noting, though, that many norms that were once believed to be set in biological stone or ordained by gods have been changed by societies – sometimes remarkably quickly. If we invented it, we can alter it. An accepted “natural” state that has existed for millennia can be changed in mere months.

Transcendence: How Humans Evolved Through Fire, Language, Beauty, and Time by Gaia Vince is published by Allen Lane. To order a copy go to Free UK p&p on all online orders over £15.

This article was amended on 5 November 2019 to remove Steven Pinker’s name from the subheading, which mischaracterised his position.

The Future Women in Tech Offers a Rosier Forecast

More tech-based classes for females

Luckily, schools, colleges and organizations across the country are working to increase their education and exposure of women to tech. First off, a growing number of K-12 schools across the U.S. have begun to offer STEM programs for school-aged girls.

The number dropped to 37% by 2019, but Harvard introduced a Women in Computer Science program, where members receive special advising, networking events, and mentorship.

Harvard also launched &ldquoWECode,&rdquo the largest student-run conference for women in computer science. Other schools and universities across the U.S. &mdash such as these for instance &mdash initiated similar programs to bring more women into technical roles and fields.

On top of that, a handful of universities and colleges that included Harvard, Manchester Community College, Harvey Mudd College, and Loyola University Chicago launched WiSTEM (Women&rsquos Inclusion in Science, Technology, Engineering & Mathematics).

WiSTEM aims to familiarize women with the challenges and opportunities faced by women entrepreneurs. It offers mentorship by top male and female business leaders and connects participants to leading venture companies.

More competitions for females

When it comes to competitions for women in tech, one of the most notable initiatives is sponsored by NRG Energy called FIRST.

Children who can&rsquot afford college are given scholarships to fund, design, brand, build, and program industrial-size robots. More than 33% of female participants have gone on to study engineering, and FIRST has made it their mission to support #WomenInSTEM.

Similarly, the Conrad Challenge, a program of the Conrad Foundation, encourages students aged 13-18 to innovate responses to issues in aerospace and aviation, cyber-technology and security, energy and the environment, and health and nutrition.

More than half of Conrad Challenge participants are girls.

In an interview for Foundation for a Smoke-Free World, Founder and CEO of Conrad Foundation, Nancy Conrad noted that women love to have an impact on society. Women are incredibly talented at visioning and understanding how the dots connect. Women tend to think of circles rather than linear ways. Conrad believes that type of thinking is baked into our DNA.

For older post-high school-age females who lacked the chance to go to university, or who want to straddle home with work, a growing number of online courses and sites, such as SkillCrush, offer the chance to learn tech skills that can help them earn an income from home.

MeToo for women in Tech &mdash the growth of female-designed products

October, 16, 2019, Britainś Culture Secretary, Nicky Morgan, warned tech executives in London that technology designed and built by men is: &ldquoMaking life harder for women and can lead to physical harm&rdquo.

Her examples included devices like smartphones that are designed according to men&rsquos hands, rather than women&rsquos hands, as well as crash test dummies that represented men&rsquos bodies, resulting in women more likely to be injured in a car crash.

Over in the U.S., this same workforce imbalance was represented by Apple that initially released its Health App with the intent of making almost every aspect of your body&rsquos daily functions quantifiable.

There was only one slight problem &mdash Apple forgot to take women&rsquos menstrual or reproductive cycle into account!

Once this was pointed out, Apple instantly redesigned its Health App &mdash still it&rsquos another example of the extent to which the STEM industry is male-dominated and lacks a female perspective.

To take one instance: toys that teach coding are largely designed by men and, therefore, tend to appeal to boys. To rectify that situation, SmartGurlz, launched in 2011, teaches girls to code robots within 60 seconds with its attractive pink devices that have doll-like features.

The company boasts: "The software is specially designed to engage your daughter's brain in the right way for her to learn&hellip SmartGurlz Inspires the female leaders of tomorrow, today. Our mission is to create the creators of the future, one girl at a time."

Mindset reboot about women in tech for women

Would you believe that America&rsquos first Beauty Queen has brains and brawn as well as beauty!

Well, here's the news, quoting MSN: Miss Virginia Camille Schrier earned the title of Miss America 2020 on Thursday night, beating out 50 other contestants for the prestigious crown after performing the show&rsquos first-ever science demonstration in the talent portion.

This next-generation Beauty Queen put the lie to the stereotype that blond or sexy women lack brains or that studious or intelligent women are dull and dowdy.

For both genders, that&rsquos the mindset reboot that a womanś beauty can co-exist with intelligence &mdash that one quality does not preclude the other.

As for women, they're starting to learn that not only is tech important but that it can also help them make a difference in the world.

A recent Microsoft study found that 72% of its female respondents rejected a career in high-tech on the grounds that they wanted a job that helps the world, and they considered technology didn't fit that representation.

That mindset is beginning to change with women like Whitney Wolfe, who founded dating app Bumble followed by Tinder.

&ldquoThe reason I started Bumble was because I wanted a solution to the experience I went through and it was something that I could see many women face,&rdquo she told Machine Design, a resource for mechanical engineers. "So, I built a dating app where only women could make the first move, and we built a brand that was built on the foundations of female first, empowerment and respect.&rdquo

Mindset reboot about women in tech for men

Then there&rsquos the mindset reboot for men.

A mere four years ago, James Damore, an engineer at Google, was fired for posting a memo claiming that women were biologically and psychologically less suited to working in high-tech career fields.

The idea that women are disproportionately capable of working in STEM fields is a tired stereotype and propagated by individuals who use &ldquoscientific backing&rdquo to argue that women are disqualified by reason of brain structure, inherent psychological traits, or even temperament.

Want proof that the ideology is changing?

Not only was our Miss America 2020 chosen partly because she showed her judges how hydrogen peroxide decomposes, but Mission Unstoppable, a new CBS series, showcases leading women in STEM.

These include superstars in the fields of science, technology, engineering and math. On top of that, more than 200 professional cheerleaders for the NBA, NFL and UFL banded to form the Science Cheerleaders.

Their mission is to encourage women to consider careers in technology and to &ldquoplayfully&rdquo challenge science and cheerleading stereotypes.

Declining salary gap for women in tech

And reports, such as these by Glassdoor and the US Bureau of Labor Statistics, suggest this pattern will continue. That&rsquos partly thanks to companies such as Intel and Salesforce that make concerted efforts to bridge the gap.

The shrinking wage gap is also due to women&rsquos new-found confidence in their STEM skills. Furthermore, as Women in Tech notes: &ldquoWith women with less than two years of experience better at negotiating pay than their male coworkers and a continuing skills shortage in the tech sector, it seems that the only way is up for rates of pay for female techies.&rdquo

Indeed, while robots displace some jobs, artificial intelligence (AI) can&rsquot replace positions that need emotional skills in which females typically excel &mdash and this extends to tech, too.

Some futurists forecast that female applicants would be offered competitive pay.

There&rsquos no doubt that the world of work is changing and while some reports project that women may have trouble competing in high-tech jobs and salary with men, others suggest that women will join the high-tech force in larger numbers than before and enjoy improved wages, according to a 2019 literature review by the Institute for Women&rsquos Policy Research.

Endless opportunities for women in tech

Improved changes for women in tech aren&rsquot confined to female-oriented STEM programs, a shrinking pay gap, female-designed tech products or a reformed perception of women&rsquos intelligence.

Women of the future will find more and better-paying job opportunities in tech, too.

Women in Tech cited a 2019 survey, where more than half of 1,500 women aged between 18 and 39 who worked in technology said they were recently given opportunities to advance in IT.

Meanwhile, some of technology&rsquos biggest companies, including Apple, Facebook, Google and Intel, have pledged to improve the future of women in both low and high-tech.

Google, Apple, and FaceBook, for example, have launched diversity campaigns in recent years. Others, such as financial software giant Intuit, focus on retaining and attracting women to their workforces.

It&rsquos &ldquoWomen in Tech&rdquo blog interviews successful women technologists promotes events for female technologists and talks about how women can &ldquochange the future of technology together&rdquo, among other items.

Eli Lilly, a pharmaceutical company, offers its female technicians daycare. These top eight companies for women in tech strive to give their female employees flexible work structure, great compensation, quality commissions and a positive environment.

As Dave Gibbs, STEM computing and technology specialist at the National STEM Learning Centre and Network told TechWomen: &ldquoThere is so much opportunity out there for women going to work in technology. Now that the companies are beginning to wake up to the value of women in the industry, both as customers and employees, there are endless possibilities to explore.&rdquo

More venture company funding for women in tech

While Veena Gundavelli found it excruciating to win funding for Emagia more than 20 years ago &mdash times they are a changin'.

True, a recent YouGov survey found that women-run businesses receive only 9% of funding for US startups, while qualified female founders receive no more than a pithy 2.2% of VC investment.

Still Pip Jamieson, founder and CEO of creative networking platform The Dots, promised better times ahead: &ldquoToday. there are alternative funding vehicles that women can capitalize on, such as crowdfunding platforms like Kickstarter and crowd investment platforms like Seeders & Crowdcube. There are also amazing organisations like Angel Academe, (an angel network that only invests in businesses founded or co-founded by women).&rdquo

Jamieson also spoke of forward-thinking venture capitalists who actively pursue female founders and hire more female investment partners to root out bias from their investment processes.

In Canada, there's also the $200 million Women In Technology (WIT) Venture Fund that aims to double the number of majority women-owned businesses in that country by 2025

&ldquoThings won&rsquot change overnight,&rdquo Jamieson concluded. &ldquoBut being an optimist I&rsquom beyond excited for the future!&rdquo

Emergent Questions

Based on our analysis of the data, the following key questions arose:

  • What are the factors that draw women toward and/or keep women from entering management-level and C-level positions?
  • What are the implications for companies, in the AI and ML space and beyond, with more or less female leaders?

To help us begin to address these questions, we again sought out reputable sources, including research groups and thought leaders who have addressed the topic, as well as female C-level executives themselves.

What are the factors that draw women toward and/or keep women from entering management-level and C-level positions?

In a survey based on 1,000 male/female respondents, Author Tara Mohr for the Harvard Business Review found that 78% of women’s reasons for not applying for a position have to do with “believing that the job qualifications are real requirements and seeing the hiring process as more by-the-book and true to the on paper guidelines than it really is.” Towards the close of her analysis, Mohr gives a memorable reflection of her own experience as a woman striving to find a place of leadership in enterprise:

“When I went into the work world as a young twenty-something, I was constantly surprised by how often, it seemed, the emperor had no clothes. Major decisions were made and resources were allocated based not on good data or thoughtful reflection, but based on who had built the right relationships and had the chutzpah to propose big plans. It took me a while to understand that the habits of diligent preparation and doing quality work that I’d learned in school were not the only—or even primary—ingredients I needed to become visible and successful within my organization.”

Mohr’s realization of the importance of network and relationships is an essential point, and one that is illuminated by McKinsey’s “ Women in the Workplace” findings—that 90% of new CEOs for the S&P 500 were promoted or hired from similar line roles. It seems a fair hypothesis that outside of founding one’s own company, which several of our documented CEO’s had done singularly or as a co-founder, working one’s way up the ladder and forming the right relationships is key to evolving as a leader.

In a 2012 report that explored the “talent pipeline and gender-diversity practices” amongst Fortune 200 companies (selected based on outlined evidence of their promoting gender diversity), McKinsey found that while more women are making it to mid-level management, less move on to executive roles and C-level positions. One key point of analysis is the need for companies to find ways to retain talent. There is a strong correlation of drop-off in females in leadership roles, especially as women move through child-bearing years.

Not all companies are created equal some have found ways to attract, keep, and promote more women than others, and it was based on interviews with senior executives from these companies that McKinsey identified best practices for ensuring closure of the gender gap in leadership, which they outline: senior leaders being consciously aware and committed to achieving gender-diversity integrating this awareness into their talent management processes measuring progress against goals and maintaining an ongoing spotlight on the issue.

McKinsey suggests placing a “focus on removing barriers that discourage all but the most resilient women,” a character trait that bears importance for both men and women successful in assuming leadership roles. Echoing this idea in response to one of our questions, StoryStream CSO Janet Bastiman remarked, “I’ve met some of the dinosaurs of business along the way, been patronized at times and even had some colleagues try to demoralize me. If I was held back at a company, I had the tenacity to find a better one where there was better opportunity. “

From Computer Girls to Computer Geeks

In 1984, the percentage of women in computer science in the United States flattened, and then plunged, even as the share of women in other technical and professional fields kept rising .

According to a New York Times report , the percentage of women, and even minorities, in IT is still declining.

Source: National Science Foundation, American Bar Association, American Association of Medical Colleges
Credit: Quoctrung Bui/NPR

These “computer girls” were overtaken by “computer geeks” as the domineering stereotype.

At Gender News, Brenda D. Frink explains how “computer geek” overtook “computer girl” as the stereotype. She writes:

As late as the 1960s many people perceived computer programming as a natural career choice for savvy young women. Even the trend-spotters at Cosmopolitan Magazine urged their fashionable female readership to consider careers in programming. In an article titled “The Computer Girls,” the magazine described the field as offering better job opportunities for women than many other professional careers. As computer scientist Dr. Grace Hopper told a reporter, programming was “just like planning a dinner. You have to plan ahead and schedule everything so that it’s ready when you need it…. Women are ‘naturals’ at computer programming.” James Adams, the director of education for the Association for Computing Machinery, agreed: “I don’t know of any other field, outside of teaching, where there’s as much opportunity for a woman.”

Managers back in the 60s simply saw computer programming as an easy job that it was like typing or filing and that software development was less important than hardware development.

It turns out programming is hard, and women are actually just as good at it as men.

On the part of the male programmers, there was a deliberate, concerted effort to elevate their work out of the “women’s work” category.

They formed professional associations and discouraged the hiring of women. There were even advertisements framing women as error prone and inefficient.

This 1960s advertisement targeted women computer operators for replacement by upgraded technology

Recruitment examinations (usually in the form of mathematical puzzle tests) were intentionally designed to slant in favor of men who had taken math classes. Even personality tests that purported to find the ideal “programming type” slanted to favor men. Frink, citing Ensmenger, writes:

According to test developers, successful programmers had most of the same personality traits as other white-collar professionals. The important distinction, however, was that programmers displayed “disinterest in people” and that they disliked “activities involving close personal interaction.” It is these personality profiles, says Ensmenger, that originated our modern stereotype of the anti-social computer geek.

Moreover, it is theorized that the decline in share of women in computer science in the United States started falling at roughly the same moment when personal computers started showing up in US homes in significant numbers.

Personal computers in the 1980s were considered toys its utility was limited, offering little more than games and word processing. But these computers were marketed to boys (see ‘ 80’s Radio Shack Color Computer Commercial ‘).

TV commercials perpetuated the idea that computers were “boys’ toys”. Women were not the target market. And what do you do when a commercial doesn’t relate to you? You disconnect. The more you see these commercials, the more you start making connections between objects and subjects. Computer. Boy. Doll. Girl. This does not mean that women and men are passive consumers. We are fully capable of recognizing and rejecting subliminal messages. But it would be naive to ignore the idea that nearly everything, through discourse, is socially constructed – including gender norms.

In the 1980s, films like Revenge of the Nerds (1984) and War Games (1983) seemed to stereotype computer programmers as intelligent young, white men. This stereotype has re-appeared in films like Hackers (1995) and The Social Network (2010). Whether or not the hacker is portrayed as a villain destroying proprietary computer systems or heroes using code to identify criminals, the myth of ‘The Hacker’ (i.e. the genius coder) less commonly involved people from marginalized groups (like women). We are increasingly seeing gender and racial diversity in the technology industry (and in media representations of the tech industry), but these people often float in the background.

Revenge of the Nerds (1984)

Women’s invisibility can further be observed even in the American literature. This was done in so many ways such as minimizing this type of work, classifying the occupation as clerical in nature, and altogether subprofessional. Or, totally make women invisible.

Consider this snippet from a 1948 article in Popular Science Monthly:

In the above article, the ENIAC is pictured with a male operator. Bu the ENIAC was actually operated by a vast majority of women. It was typical of much of the imagery from the early era of electronic computing to omit women who were often integral to the operation and substitute them for men.

Study 2: Important Traits to Succeed in Leader vs. Assistant Roles

In Study 2, we had participants imagine themselves in either a leadership or assistantship role and examined the extent to which they believed they would need to act in agentic and communal ways in order to be successful in that role. To our knowledge, the present study was the first one to examine adult men’s and women’s beliefs about the traits they would need to be successful in a randomly assigned leader role. As such, this study is particularly well suited to establish a direct causal link between occupying a leadership role and differentially valuing agentic and communal traits.

We expected that agentic traits, including competence and assertiveness, would be rated as more important to succeed in a leader role, but as less crucial for assistant roles. In contrast, we expected participants to see communal traits, such as patient and polite, as more important to be a successful assistant than a successful leader. Moreover, although previous research has shown that agency is more desirable than communality in the self (as compared to in others) (Abele and Wojciszke, 2007), we predict that the role will influence the extent to which people find agentic traits desirable in the self. Specifically, whereas we expected that agency would take precedence over communality for participants in the leader role, we expected to find the reverse for those in the assistant role, for whom communality would take precedence over agency.

We anticipated that both male and female participants would rate agentic traits (like competence and assertiveness) as more important to succeed as a leader than communality, similar to past investigations (Koenig et al., 2011). However, we also anticipated an interaction between role and participant gender, such that women compared to men would rate communal traits as more important to succeed as a leader. This is because people tend to favor traits and attributes that are characteristic of their in-groups (versus attributes that are not, or that characterize an outgroup) (Dovidio and Gaertner, 1993), and because women compared to men have been found to possess less masculine leader-role expectations (Boyce and Herd, 2003 Koenig et al., 2011) and to value female leaders more (Kwon and Milgrom, 2010 Vial et al., 2018).



The study employed a 2൲൳ mixed design with participant gender (male vs. female) and role condition (leader vs. assistant) as between-subjects factors and trait category (competence, assertiveness, and communality) as a within-subjects factor. We enrolled 252 MTurk participants with a HIT completion rate of 95% or higher, who were compensated

Limitations and Remaining Questions

Although the random assignment of men and women to a leader (vs. assistant) role in Study 2 allowed us to extend past investigations by drawing causal links between roles and trait desirability, a potential limitation in our approach is that the role manipulation may also conceivably lead to a difference in psychological feelings of power across conditions (Anderson and Berdahl, 2002 Schmid Mast et al., 2009). Given the large conceptual overlap between leadership and “power” (commonly defined as asymmetric control over resources Keltner et al., 2003), it is possible that the results of Study 2 reflect at least in part the way men and women feel when they are in a position of power, independently from their role as leaders or assistants. Future investigations may address this issue by measuring felt power (Anderson et al., 2012) to examine whether participants value similar traits as they did in Study 2 over and above felt power. For example, it is conceivable that individuals in leadership roles that foster stronger (vs. weaker) feelings of power might value communality to a lower extent, and behave more dominantly overall (e.g., Tost et al., 2013).

Another potential limitation in Study 2 is that participants assigned to the assistant role condition might have assumed that the team leader was male𠅌onsistent with the notion that people think “male” when they think “manager” (Schein, 1973). Therefore, it is unclear whether the traits that they thought would help them be a successful assistant would be contingent on the assumption that they would be assisting a male-led team. Future investigations may probe whether people believe that it takes different attributes to successfully work for a female versus a male leader, and how those beliefs impact their support for male and female supervisors. For example, if men think that a female leader would expect more cooperation from subordinates than a male leader, this expectation may partly explain their reluctance to work for women.

It is also worth noting that, in both studies, we did not specify the context under which leadership (and, in Study 2, assistantship) was taking place. It seems likely that participants were thinking of some traditionally male-dominated domain (as businesses typically are). However, one important next step for future work is to examine whether the leadership domain affects which traits people value in leaders, and which traits they would find valuable for them, personally, to be a successful leader. Leaders tend to be considered particularly effective in industries and domains in which the gender composition is congruent with the gender of the leader (Ko et al., 2015 see also Eagly et al., 1995). It is conceivable that being the leader of a team that is working in a traditionally feminine domain (e.g., childcare, nursing, or even a business that caters primarily to women, such as maternity-wear or cosmetics) might change people’s perception of which traits are most important.

Whereas our investigation was focused on the general dimensions of agency and communality (Abele et al., 2016), future research might adapt the methodology of Study 1 to examine the potential tradeoffs between other kinds of leader attributes. For instance, past research has examined task-oriented versus person-oriented trait dimensions (Sczesny et al., 2004), traits related to activity/potency (e.g., forceful, passive Heilman et al., 1995), “structuring” versus 𠇌onsideration” behaviors (Cann and Siegfried, 1990 Sczesny, 2003), and transformational leader traits (Duehr and Bono, 2006), to name a few. In particular, given that transformational leadership styles tend to be quite favorable in contemporary organizations (Wang et al., 2011), and are more closely associated with femininity (Kark et al., 2012 Stempel et al., 2015), it would be especially interesting to examine whether such transformational leader attributes are also considered “unnecessary frills” (much like communal attributes in Study 1). As mentioned earlier, the context of leadership (more male- vs. more female-dominated) may be an important moderating factor worthy of consideration (Ko et al., 2015). For example, male followers appear to react more negatively to transformational leadership styles compared to female followers (Ayman et al., 2009). Thus, it is possible that the tradeoff between more and less transformational leadership attributes may partly depend on the specific industry or domain.

Similarly, whereas we examined two sub-dimensions of agency (i.e., competence and assertiveness) following Abele et al. (2016), we did not distinguish different facets within the dimension of communality. Specifically, research suggests that communality may be broken into sub-dimensions of warmth or sociability (e.g., friendly, empathetic) and morality (e.g., fair, honest) (Abele et al., 2016), a distinction that may be meaningful and consequential in the evaluation of leaders. It has been argued that morality in particular, more so than warmth/sociability, plays a primary role in social judgment (Brambilla et al., 2011 Brambilla and Leach, 2014 Leach et al., 2017), and moral emotions are implicated in bias against agentic female leaders (Brescoll et al., 2018). Thus, future investigations may examine how the tradeoff between agency and communality explored in our research might change when the morality facet of communality is considered separately from the warmth/sociability facet.

Additional research may extend the current investigations by adapting the methodology we employed in Study 1 (which we, in turn, adapted from Li et al., 2002) in various ways to further examine leader-role expectations and preferences for communality and agency in leaders (both in others and in the self). Whereas we did this in the current investigation by testing the potential tradeoffs between ideal levels of communal and agentic traits (Study 1) and the extent to which men and women viewed those traits as personally important to succeed in a leader (vs. assistant) role (Study 2), it would be worthwhile to merge these two paradigms in the future. For example, men and women in leadership roles might be asked to think about the traits they would need to be successful and then to “purchase” various amounts of those traits for themselves. Similarly, participants could be asked to purchase traits to design the ideal leader versus the ideal subordinate (e.g., the perfect assistant).

.55. The study took approximately 10 minutes and was described to potential participants as a research study about personal experiences, feelings, and attitudes. Three participants (1.2%) indicated that some of their answers were meant as jokes or were random. We report analyses excluding these 3 participants (n = 249 mean age = 32.55, SD = 11.88 42.6% female 71.9% White). One participant (0.4%) did not indicate gender. A sensitivity power analysis using G ∗ Power 3.1 (Faul et al., 2007) showed a sample of this size (n = 249) is sufficient to detect a small interaction effect between within- and between-factors, i.e., f(U) = 0.169 with power = 0.80 and f(U) = 0.208 with power = 0.95 (assuming α = 0.05, four groups, and 3 repeated measures).


Participants first read a short vignette asking them to imagine that they were part of a team working on an important project. The full text of the vignette is presented in Appendix B. Half of participants were randomly assigned to a role condition in which they imagined being the team leader, and the other half were assigned to a role condition in which they imagined being the assistant to the leader. All participants were asked to indicate how important each of a series of attributes was to be successful in their role. Specifically, for each trait, they read 𠇊s [a leader/an assistant] it is important to be [trait],” and indicated their answer from 1 (not at all) to 7 (extremely so). The list of traits, all of which were used in Study 1, included eight agentic traits, three of which measured competence (i.e., competent, confident, capable α = 0.75), and five of which measured assertiveness (i.e., ambitious, assertive, competitive, decisive, self-reliant α = 0.78), and eight communal traits (i.e., cheerful, cooperative, patient, polite, sensitive, tolerant, good-natured, sincere α = 0.83). 1 Finally, all participants were asked basic demographic questions (e.g., age, race), and received a debriefing letter.


We conducted a mixed-model ANOVA with participant gender and experimental role condition as between-subjects factors, and trait category (competence, assertiveness, and communality) as a repeated measure. As expected, we found a significant interaction between role and trait category, F(2,243) = 32.31, p < 0.001, ηp 2 = 0.210. The interaction between participant gender and trait category was not significant, F(2,243) = 1.85, p = 0.159, nor was the 3-way interaction between trait category, role, and participant gender, F(2,243) = 1.19, p = 0.306.

All means are represented in Figure 2.

FIGURE 2. Mean ratings of importance to succeed in a randomly assigned assistant versus leader role for all trait dimensions in Study 2. Error bars represent the standard error of the mean.

Pairwise comparisons revealed that participants in the leader role rated both competence, MD = 0.242, SE = 0.09, 95% CI [0.056, 0.428], p = 0.011, and assertiveness, MD = 0.839, SE = 0.12, 95% CI [0.599, 1.078], p < 0.001, as significantly more important to succeed compared to participants in the assistant role. In contrast, communality was rated as significantly more important to succeed as an assistant than as a leader, MD = -0.218, SE = 0.10, 95% CI [-0.422, -0.013], p = 0.037.

Looking at it another way, participants in both the leader and assistant roles rated competence as the most important set of traits, higher than assertiveness (MD = 0.794, SE = 0.09, 95% CI [0.627, 0.961], p < 0.001 in leader role and MD = 1.391, SE = 0.09, 95% CI [1.223, 1.559], p < 0.001 in assistant role) and communality (MD = 1.085, SE = 0.07, 95% CI [0.945, 1.226], p < 0.001 in leader role and MD = 0.626, SE = 0.07, 95% CI [0.485, 0.766], p < 0.001 in assistant role). Those in the leader condition rated assertiveness as more important than communality, MD = 0.291, SE = 0.09, 95% CI [0.108, 0.474], p = 0.002, whereas those in the assistant condition did the reverse, rating communal traits as more desirable than assertive ones, MD = -0.765, SE = 0.09, 95% CI [-0.949, -0.581], p < 0.001.


The goal of Study 2 was to examine men’s and women’s beliefs about the traits that would be important to help them personally succeed in a randomly assigned leader (vs. assistant) role. As expected, results supported our general predictions. In line with past work (Koenig et al., 2011), people rated competence and assertiveness as more necessary for success as a leader (vs. assistant), and communality as more necessary for success as an assistant (vs. leader). Although competence was seen as relatively more important for leaders than for assistants (as would be expected for a high-status professional role e.g., Magee and Galinsky, 2008 Anderson and Kilduff, 2009), competence emerged as the most important trait to succeed in both types of roles. Moreover, as we had anticipated, even though people tend to value agency over communality when thinking of the self (Abele and Wojciszke, 2007), role assignment had the effect of reversing this pattern for participants in the assistant role (at least in terms of assertiveness, which assistants rated as less important for them to succeed than communality).

Even though we had expected to find that women (vs. men) would value communal traits to a higher extent (Boyce and Herd, 2003 Koenig et al., 2011), women were just as likely as men to see these traits as relatively unimportant for them personally to be successful in leader roles, and we failed to find any participant gender effects either in the leader or assistant role. This null interaction effect—which stands in contrast to the gender differences we observed in Study 1—might reflect the power of role demands to change self-views (Richeson and Ambady, 2001) and to override the influence of other factors such as category group memberships (LaFrance et al., 2003). Moreover, it is possible that, even if women valued communality more so than men when thinking about other leaders, they may nevertheless feel as though acting in a stereotypically feminine way and behaving less dominantly than a traditional male leader would place them at a disadvantage relative to men (Forsyth et al., 1997 Bongiorno et al., 2014). Such self-versus-other discrepancy might explain why the expected gender difference in the appreciation of communality relative to agency-assertiveness emerged in Study 1, when participants were thinking of ideal leaders, but was not apparent in Study 2, when participants were asked to think about themselves in a leader role.

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