Invisible Women: Data Bias in a World Designed for Men

Wrong. That would be to assume that men face the same heat for being seen as cold. They don’t. The 2010 study didn’t just find that female politicians were see as less caring. It found that this perception inspired moral outrage in both male and female study participants, who viewed such women with contempt, anger and/or disgust. This was not the case for their male counterparts. Molly Crockett, associate professor of experimental psychology at Oxford University, has an explanation for this disparity: being seen as uncaring is a norm violation for women in a way that it just isn’t for men. ‘There is an expectation’, she tells me, ‘that on average women are going to be more pro-social than men.’ Any deviation by a woman from what is seen (no matter how illogically) as a ‘moral’ stance therefore shocks us more.

Given the clear significance of gender when it comes to these issues, you would hope that this might be an area of research that bucks the gender-data-gap trend. It does not. Imagine my excitement when I came across a paper published in January 2017 entitled ‘Faced with exclusion: Perceived facial warmth and competence influence moral judgments of social exclusion’.19 Given the findings of Fiske and Cikara about women’s warmth/competence trade-off this should have been an extremely useful paper. As the authors explain, ‘people’s moral judgment about social exclusion can be influenced by facial appearance, which has many implications in intergroup research’. That is, people’s decisions about whether or not it’s fair that someone is being ostracised or bullied can be influenced by what the victim looks like.

Indeed. Unfortunately, the study authors ‘used male faces only for reasons of test efficiency’, making the study absolutely worthless when it comes to the group most affected by this issue, i.e. women. Fiske and Cikara explain that gender, ‘is a salient, and perhaps the most salient, social category’, with gender stereotyping often being immediate and unconscious: ‘the mere sight of a woman can immediately elicit a specific set of associated traits and attributions, depending on the context’. Still, at least the test was efficient.

‘It’s actually kind of shocking how little attention there’s been to gender in the morality literature,’ says Crockett. But on the other hand, maybe it’s not: the study of morality, Crockett tells me, is ‘really aiming at trying to uncover human universals’. At the point she mentions ‘universals’, of course, male-default-thinking alarm bells start ringing in my head. Many academics in the field of morality subscribe to ‘very egalitarian, utilitarian, impartial views of what is right’, Crockett continues, and they perhaps impose those norms ‘onto the research that we do’. The alarm bells ring off the hook.

But the next thing she says provides something of an explanation for how male-default thinking could be so prevalent in a world that is, after all, 50% female. ‘It’s just a feature of human psychology,’ she explains, to assume that our own experiences mirror those of human beings in general. This is a concept in social psychology that is sometimes called ‘naive realism’ and sometimes called ‘projection bias’. Essentially, people tend to assume that our own way of thinking about or doing things is typical. That it’s just normal. For white men this bias is surely magnified by a culture that reflects their experience back to them, thereby making it seem even more typical. Projection bias amplified by a form of confirmation bias, if you like. Which goes some way towards explaining why it is so common to find male bias masquerading as gender neutrality. If the majority of people in power are men – and they are – the majority of people in power just don’t see it. Male bias just looks like common sense to them. But ‘common sense’ is in fact a product of the gender data gap.

Mistaking male bias for impartial, universal, common sense means that when people (men) come across someone trying to level the playing field, it’s often all they can see (perhaps because they read it as bias). A 2017 paper found that while white male leaders are praised for promoting diversity, female and ethnic minority leaders are penalised for it.20 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. And so all the stereotypes that go along with that become salient: bossy, assertive, cold and all the rest. Conversely, ethnic minority and female leaders ‘avoid negative stereotypes when they engage in low levels of diversity-valuing behavior’. At last, empirical proof for what most women (even if they don’t admit it to themselves) have always known, at least implicitly: playing along with patriarchy is of short-term, individual benefit to a woman. There’s just the minor issue of being on borrowed time.

The finding that engaging in ‘diversity-valuing behavior’ reminds people that a woman is in fact a woman perhaps explains how Sanders came to think that all Clinton said was ‘vote for me, I’m a woman’ – because the data shows that she certainly didn’t. A word-frequency analysis of her speeches by Vox journalist David Roberts revealed that Clinton ‘mostly talked about workers, jobs, education and the economy, exactly the things she was berated for neglecting. She mentioned jobs almost 600 times, racism, women’s rights and abortion a few dozen times each.’ But, pointed out US writer Rebecca Solnit in her London Review of Books piece on the election, ‘she was assumed to be talking about her gender all the time, though it was everyone else who couldn’t shut up about it’.21


What all of this means on a grander scale is that democracy is not a level playing field: it is biased against electing women. This is a problem, because male and female legislators inevitably bring different perspectives to politics. Women lead different lives to men because of both their sex and their gender. They are treated differently. They experience the world differently, and this leads to different needs and different priorities. Like a male-dominated product-development team, a male-dominated legislature will therefore suffer from a gender data gap that will lead it to serve its female citizens inadequately. And most of the world’s governments are male-dominated.

As of December 2017, women made up an average of 23.5% of the world’s parliamentarians, although this figure hides significant regional variation: Nordic parliaments are on average 41.4% female while Arab parliaments are on average 18.3% female.22 Women account for 10% or less of parliamentarians in thirty-one countries, including four countries that have no female parliamentarians at all. And in most countries precious little is being done to remedy this.

In 2017 the UK’s Women and Equalities Committee produced a report with six recommendations for the government to increase female representation in Parliament.23 They were all rejected.24 One of the recommendations was for the government to allow all-women shortlists (AWS) in local as well as general elections, and to extend their legality beyond the current 2030 cut-off point. In the British system, each political party holds an internal election for every constituency to decide which candidate will stand for them in a general election. AWS are used in these internal elections if a party wants to ensure that their general-election candidate will be a woman.

AWS were first used in the UK’s 1997 elections. In January 1997, the United Kingdom tied with St Vincent & the Grenadines and Angola in the world rankings of female parliamentarians.25 With a 9.5% female House of Commons, they all sat in joint fiftieth place. But by December of the same year the UK had suddenly shot up to twentieth place, because in May it had an election. And in that election the Labour Party, the UK’s main opposition party, made use of AWS for the first time. The effect was dramatic. The number of female Labour MPs leapt from thirty-seven to 101 (the overall rise in female MPs was from sixty to 120).

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