Invisible Women

Key Concepts

Our culture and history makes men the “default”

“The result of this deeply male-dominated culture is that the male experience, the male perspective, has come to be seen as universal, while the female experience – that of half the global population, after all – is seen as, well, niche.” (p. 12)

“None of this means that the Bank of England deliberately set out to exclude women [from representation on currency]. It just means that what may seem objective can actually be highly male-biased: in this case, the historically widespread practice of attributing women’s work to men made it much harder for a woman to fulfil the Bank’s requirements. The fact is that worth is a matter of opinion, and opinion is informed by culture. And if that culture is as male-biased as ours is, it can’t help but be biased against women. By default.” (p. 17)

“The way whiteness and maleness go without saying brings me back to my bad date (OK, dates), because it is intrinsically linked to the misguided belief in the objectivity, the rationality, the, as Catherine Mackinnon has it, ‘point-of-viewlessness’ of the white, male perspective. Because this perspective is not articulated as white and male (because it doesn’t need to be), because it is the norm, it is presumed not to be subjective. It is presumed to be objective. Universal, even.” (p. 24)

City planners don’t take into account the life patterns of women when making policy decisions

“While much of the historical gender data gap in travel planning has arisen simply because the idea that women might have different needs didn’t occur to the (mainly) male planners, there is another, less excusable, reason for it, and that is that women are seen as, well, just more difficult to measure. ‘Women have much more complicated travel patterns,’ explains Sánchez de Madariaga, who has designed a survey to measure women’s care travel. And on the whole, transport authorities aren’t interested in women’s ‘atypical’ travel habits.” (p. 38)

“Zoning laws are based on, and prioritise the needs of, a bread-winning heterosexual married man who goes off to work in the morning, and comes home to the suburbs to relax at night.” (p. 40)

“The apparent mismatch between women’s fear and the level of violence the official statistics say the experience is not just about the general stew of menace women are navigating. Women also aren’t reporting the more serious offences. A 2016 survey of sexual harassment in the Washington DC metro found that 77% of those who were harassed never reported, which is around the same level found by Inmujeres, a Mexican government agency that campaigns on violence against women.” (p. 56)

“When planners fail to account for gender, public spaces become male spaces by default.” (p. 66)

Workplaces do not account for the fact that women have more responsibilities outside of work and, instead, believe there to be a meritocracy

“Sex-disaggregated data is not available for all countries, but for those where the data exists, the trend is clear. In Korea, women work for thirty-four minutes longer than men per day, in Portugal it’s ninety minutes, in China it’s forty-four minutes, and in South Africa it’s forty-eight minutes.” (p. 72)

“But a workplace predicated on the assumption that a worker can come into work every day, at times and locations that are wholly unrelated to the location or opening hours of schools, childcare centres, doctors and grocery stores, simply doesn’t work for women. It hasn’t been designed to.” (p. 86)

“Companies also still seem to conflate long hours in the office with job effectiveness, routinely and disproportionately rewarding employees who work long hours. This constitutes a bonus for men.”(p. 87)

“Actually, a belief in meritocracy may be all you need – to introduce bias, that is. Studies have shown that a belief in your own personal objectivity, or a belief that you are not sexist, makes you less objective and more likely to behave in a sexist way” (p. 94)

“That the myth of meritocracy survives in the face of such statistics is testament to the power of the male default: in the same way that men picture a man 80% of the time they think of a ‘person’, it’s possible that many men in the tech industry simply don’t notice how male-dominated it is. But it’s also testament to the attractiveness of a myth that tells the people who benefit from it that all their achievements are down to their own personal merit. It is no accident that those who are most likely to believe in the myth of meritocracy are young, upper-class, white Americans.” (p. 95)

Women face dangers in workplaces in which occupational health data excludes them and safety equipment is not designed for them

“And so, because business leadership is still so dominated by men, modern workplaces are riddled with these kind of gaps, from doors that are too heavy for the average woman to open with ease, to glass stairs and lobby floors that mean anyone below can see up your skirt, to paving that’s exactly the right size to catch your heels.” (p. 113)

“While serious injuries at work have been decreasing for men, there is evidence that they have been increasing among women.7 The rise in serious injuries among female workers is linked to the gender data gap: with occupational research traditionally having been focused on male-dominated industries, our knowledge of how to prevent injuries in women is patchy to say the least.” (p. 114)

“Men and women have different immune systems and different hormones, which can play a role in how chemicals are absorbed.18 Women tend to be smaller than men and have thinner skin, both of which can lower the level of toxins they can be safely exposed to. This lower tolerance threshold is compounded by women’s higher percentage of body fat, in which some chemicals can accumulate.” (p. 116)

“There is no doubt that women are dying as a result of the gender data gap in occupational health research. And there is no doubt that we urgently need to start systematically collecting data on female bodies in the workplace.” (p. 120)

“The next, and crucial step, is for governments and organisations to actually use that data to shape policy around it. This isn’t happening.” (p. 120)

When companies have irregular employment arrangements, it often most harms women

“Zero-hour contracts, short-term contracts, employment through an agency, these have all been enticingly rebranded the ‘gig economy’ by Silicon Valley, as if they are of benefit to workers. But the gig economy is in fact often no more than a way for employers to get around basic employee rights.” (p. 132)

“The scheduling issue is being made worse by gender-insensitive algorithms. A growing number of companies use ‘just in time’ scheduling software, which use sales patterns and other data to predict how many workers will be needed at any one time. They also respond to real-time sales analyses, telling managers to send workers home when consumer demand is slow.” (p. 135)

“Women on irregular or precarious employment contracts have been found to be more at risk of sexual harassment (perhaps because they are less likely to take action against a colleague or employer who is harassing them) but as the #MeToo movement washes over social media, it is becoming increasingly hard to escape the reality that it is a rare industry in which sexual harassment isn’t a problem.” (p. 137)

Design teams that are overwhelmingly male fail to design for women who are differently shaped and sized and who have different needs than men

“Despite what academics, NGOs and expatriate technicians seem to think, the problem is not the women. It is the stoves: developers have consistently prioritised technical parameters such as fuel efficiency over the needs of the stove user, frequently leading users to reject them, explains Crewe. And although the low adoption rate is a problem going back decades, development agencies have yet to crack the problem, for the very simple reason that they still haven’t got the hang of consulting women and then designing a product rather than enforcing a centralised design on them from above.” (p. 154)

“There is plenty of data showing that women have, on average, smaller hands than men, and yet we continue to design equipment around the average male hand as if one-size-fits-men is the same as one-size-fits-all.” (p. 157)

“Voice recognition has also been suggested as a solution to smartphone-associated RSI, but this actually isn’t much of a solution for women, because voice-recognition software is often hopelessly male-biased. In 2016, Rachael Tatman, a research fellow in linguistics at the University of Washington, found that Google’s speech-recognition software was 70% more likely to accurately recognise male speech than female speech – and it’s currently the best on the market.” (p. 162)

“Of course, the problem isn’t women’s voices. It’s our old friend, the gender data gap. Speech-recognition technology is trained on large databases of voice recordings, called corpora. And these corpora are dominated by recordings of male voices.” (p. 164)

“When Apple launched its health-monitoring system with much fanfare in 2014, it boasted a ‘comprehensive’ health tracker.15 It could track blood pressure; steps taken; blood alcohol level; even molybdenum (nope, me neither) and copper intake. But as many women pointed out at the time, they forgot one crucial detail: a period tracker.” (p. 176)

“Men are more likely than women to be involved in a car crash, which means they dominate the numbers of those seriously injured in car accidents. But when a woman is involved in a car crash, she is 47% more likely to be seriously injured than a man, and 71% more likely to be moderately injured, even when researchers control for factors such as height, weight, seat-belt usage, and crash intensity. She is also 17% more likely to die. And it’s all to do with how the car is designed – and for whom.” (p. 186)

“The reason this has been allowed to happen is very simple: cars have been designed using car-crash test dummies based on the ‘average’ male.” (p. 186)

“Designers may believe they are making products for everyone, but in reality they are mainly making them for men. It’s time to start designing women in.” (p. 192)

In health care, treatments are designed around the average man, and women are often ignored by their doctors and the system as a whole

“References to the ‘typical 70 kg man’ abound, as if he covers both sexes (as one doctor pointed out to me, he doesn’t even represent men very well). When women are mentioned, they are presented as if they are a variation on standard humanity. Students learn about physiology, and female physiology. Anatomy, and female anatomy.” (p. 196)

“These gaps matter because contrary to what we’ve assumed for millennia, sex differences can be substantial. Researchers have found sex differences in every tissue and organ system in the human body, as well as in the ‘prevalence, course and severity’ of the majority of common human diseases.” (p. 198)

“Perhaps most galling from a gender-data-gap perspective was the finding that females aren’t even included in animal studies on female-prevalent diseases. Women are 70% more likely to suffer depression than men, for instance, but animal studies on brain disorders are five times as likely to be done on male animals.” (p. 205)

“It seems that Yentl syndrome may be at play again here: it is striking that so many of the stories women tell of undiagnosed and untreated pain turn out to have physical causes that are either exclusively female diseases, or are more common in women than in men.” (p. 227)

“The evidence that women are being let down by the medical establishment is overwhelming. The bodies, symptoms and diseases that affect half the world’s population are being dismissed, disbelieved and ignored. And it’s all a result of the data gap combined with the still prevalent belief, in the face of all the evidence that we do have, that men are the default humans. They are not. They are, to state the obvious, just men. And data collected on them does not, cannot, and should not, apply to women. We need a revolution in the research and the practice of medicine, and we need it yesterday. We need to train doctors to listen to women, and to recognise that their inability to diagnose a woman may not be because she is lying or being hysterical: the problem may be the gender data gaps in their knowledge. It’s time to stop dismissing women, and start saving them.” (p. 234)

Unpaid labor is not figured into GDP, which warps policy decision-making about what matters

“But there was one major aspect of production that was excluded from what came to be the ‘international convention about how you think about and measure the economy’, and that was the contribution of unpaid household work, like cooking, cleaning and childcare.”(p. 240)

“And excluding women does warp the figures. Coyle points to the post-war period up to about the mid-1970s. This ‘now looks like a kind of golden era of productivity growth’, Coyle says, but this was to some extent a chimera. A large aspect of what was actually happening was that women were going out to work, and the things that they used to do in the home – which weren’t counted – were now being substituted by market goods and services.” (p. 241)

A policy agenda that is shaped by women and that incorporates the needs of women could solve underinvestment in social infrastructure 

“In fact, the best job-creation programme could simply be the introduction of universal childcare in every country in the world.” (p. 247)

“A more dramatic government intervention than the introduction of paid parental leave would be to invest in social infrastructure. The term infrastructure is generally understood to mean the physical structures that underpin the functioning of a modern society: roads, railways, water pipes, power supplies. It doesn’t tend to include the public services that similarly underpin the functioning of a modern society like child and elder care.” (p. 248)

“But it’s not just about benefiting men over women. These male-biased benefits actually come at women’s expense, because as we’ve seen, women have to fill the resulting service gaps with their unpaid care work. In 2017, the Women’s Budget Group pointed out that at the same time that austerity measures were having a particularly severe impact on women in the UK, ‘tax giveaways disproportionately benefitting men will cost the Treasury £44bn per annum by 2020’.” (p. 262)

“Several US studies from the 1980s to the 2000s have found that women are more likely to make women’s issues a priority and more likely to sponsor women’s issues bills.1 In the UK, a recent analysis of the impact female MPs have had in Westminster since 1945 found that women are more likely to speak about women’s issues, as well as family policy, education and care.” (p. 265)

Women fare worse after natural disasters (e.g., death, violence, homelessness), even though data on their experiences is underreported  

“The irony of excluding women’s voices when it all goes wrong is that it is exactly in these extreme contexts that old prejudices are least justified, because women are already disproportionately affected by conflict, pandemic and natural disaster.” (p. 296)

“Domestic violence against women increases when conflict breaks out. In fact, it is more prevalent than conflict-related sexual violence.” (p. 296)

“For over twenty years, the Inter-agency Working Group on Reproductive Health in Crises has called for women in war zones or disaster areas to be provided with birth kits, contraception, obstetrics care and counselling. But, reports the New York Times, ‘over the past two decades, that help has been delivered sporadically, if at all’. One report found that pregnant women are left without obstetrical care, ‘and may miscarry or deliver under extremely unsanitary conditions.’” (p. 297)

“The irony of ignoring the potential for male violence when it comes to designing systems for female refugees is that male violence is often the reason women are refugees in the first place. We tend to think of people being displaced because of war and disaster: this is usually why men flee. But this perception is another example of male-default thinking: while women do seek refuge on this basis, female homelessness is more usually driven by the violence women face from men. Women flee from ‘corrective’ rape (where men rape a lesbian to ‘turn her straight’), from institutionalised rape (as happened in Bosnia), from forced marriage, child marriage and domestic violence. Male violence is often why women flee their homes in low-income countries, and it’s why women flee their homes in the affluent West.” (p. 306)

Quotables

“One of the most important things to say about the gender data gap is that it is not generally malicious, or even deliberate. Quite the opposite. It is simply the product of a way of thinking that has been around for millennia and is therefore a kind of not thinking.” (Preface)

“In naming the phenomenon that is causing so much damage to so many women’s lives, I want to be clear about the root cause and, contrary to many claims you will read in these pages, the female body is not the problem. The problem is the social meaning that we ascribe to that body, and a socially determined failure to account for it” (Preface).

“This is no doubt true of our species overall – but the reality of human-on-human lethal violence is that it is overwhelmingly a male occupation: a thirty-year analysis of murder in Sweden found that nine out of ten murders are committed by men.5 This holds with statistics from other countries, including Australia, the UK and the US. A 2013 UN homicide survey found that 96% of homicide perpetrators worldwide are male. So is it humans who are murderous, or men?” (p. 2)

“These white men have in common the following opinions: that identity politics is only identity politics when it’s about race or sex; that race and sex have nothing to do with ‘wider’ issues like ‘the economy’; that it is ‘narrow’ to specifically address the concerns of female voters and voters of colour; and that working class means white working-class men.” (p. 23)

“There is no such thing as a woman who doesn’t work. There is only a woman who isn’t paid for her work.” (p. 70)

“A recent Australian analysis found that the optimum length of paid maternity leave for ensuring women’s continued participation in paid labour was between seven months to a year,68 and there is no country in the world that offers properly paid leave for that length of time.” (p. 79)

“The implicit bias is clear: expense codes are based on the assumption that the employee has a wife at home taking care of the home and the kids.” (p. 90)

“For example, quotas, which, contrary to popular misconception, were recently found by a London School of Economics study to ‘weed out incompetent men’ rather than promote unqualified women.” (p. 109)

“The FAO estimates that if women had the same access to productive resources as men, yields on their farms could increase by up to 30%. But they don’t.” (p. 149)

“The data suggests she’s not wrong. Research published in 2018 by Boston Consulting Group found that although on average female business owners receive less than half the level of investment their male counterparts get, they produce more than twice the revenue.9 For every dollar of funding, female-owned start-ups generate seventy-eight cents, compared to male-owned start-ups which generate thirty-one cents. They also perform better over time, ‘generating 10% more in cumulative revenue over a five-year period’.” (p. 171)

“According to the FDA, the second most common adverse drug reaction in women is that the drug simply doesn’t work, even though it clearly works in men. So with that substantial sex difference in mind: how many drugs that would work for women are we ruling out at phase one trials just because they don’t work in men?” (p. 204)

“The UN estimates that the total value of unpaid childcare services in the US was $3.2 trillion in 2012, or approximately 20% of GDP (valued at $16.2 trillion that year).” (p. 242)

“The upshot of failing to capture all this data is that women’s unpaid work tends to be seen as ‘a costless resource to exploit’, writes economics professor Sue Himmelweit.” (p. 244)

“In short, the current US tax system for married couples in effect penalises women in paid employment, and in fact several studies have shown that joint filing disincentivises married women from paid work altogether (which, as we have also seen, is bad for GDP).” (p. 259)

“A 2017 paper found that while white male leaders are praised for promoting diversity, female and ethnic minority leaders are penalised for it This is partly because by promoting diversity, women and ethnic minorities remind white men that these female ethnic-minority leaders are, in fact, women and ethnic minorities.” (p. 270)

The real reason we exclude women is because we see the rights of 50% of the population as a minority interest. (p. 290)

“The consensus is clear: women are abnormal, atypical, just plain wrong.” (p. 314)