Invisible Women: Exposing Data Bias in a World Designed for Men

by Caroline Criado Perez

  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.

  The Women’s Budget Group argues that it should.62 Because, like physical infrastructure, what the WBG calls social infrastructure ‘yields returns to the economy and society well into the future in the form of a better educated, healthier and better cared for population’. Arguably then, this exclusion of care services from the general concept of ‘infrastructure’ is just another unquestioned male bias in how we structure our economy.

  Take early childhood education (ECE) and high-quality formal childcare including for very young toddlers and infants. Investment in these can actually reduce overall education spend because it lowers the level of investment required in remedial education.63 It also improves cognitive development, educational achievement and health outcomes64 for children (particularly socio-economically disadvantaged children).65 All of which increases productivity in the long run.66

  A report on two ECE pilot studies found that by the age of forty, US children who received ECE were more likely to be employed (76% versus 62%) and to have higher median annual earnings ($20,800 versus $15,300).67 They were also more likely to own homes (37% versus 28%); a car (82% versus 60%); and to have savings accounts (76% versus 50%). ECE was also found to have wider indirect effects of a lower crime rate, resulting in lower law-enforcement costs. The report concluded that investing in ECE had a greater positive impact on long-term economic growth than business subsidies, and would lead to an extra 3.5% growth in GDP by 2080.

  But despite all these potential gains, social-infrastructure investment is often overlooked, in no small part because of the data gap when it comes to unpaid work. This gender data gap has led, Nancy Folbre explains, to its ‘pay-off’ being ‘understated’.68 In fact, the pay-off could be huge. In the UK it would generate up to 1.5 million jobs, compared to 750,000 for an equivalent investment in construction. In the US, an investment of 2% of GDP in the caring industries ‘would create nearly 13 million new jobs, compared to the 7.5 million jobs that would be created by investing 2% of GDP in the construction sector’.69 And, because the care sector is (currently) a female-dominated industry, many of these new jobs would go to women – remember that increasing female employment drives GDP.

  The WBG found that investing 2% of GDP in public care services in the UK, US, Germany and Australia ‘would create almost as many jobs for men as investing in construction industries [. . .] but would create up to four times as many jobs for women’.70 In the US, where two-thirds of newly created care jobs would go to women compared to only one-third of newly created construction-sector jobs,71 this investment would increase women’s employment rate by up to eight points, reducing the gender employment gap by half.72 In the UK the investment would reduce the gender employment gap by a quarter (a correction not to be sniffed at given it is women’s jobs that have been hardest hit by austerity policies).73

  As well as increasing female paid employment (and therefore GDP) by actively creating new jobs for women, investing in social infrastructure can also increase female paid employment by reducing the amount of unpaid labour women have to do. The employment rate of UK mothers with children aged three to five is 6% lower than the OECD average. In 2014, 41% of mothers of children under four were employed full-time, compared to 82% of childless women and 84% of fathers.74 This sex disparity is partly due to societal expectations (enshrined in law via unequal maternity- and paternity-leave allowances) that the mother be the primary carer. But it’s also because of the gender pay gap: for many heterosexual couples it makes financial sense for the woman to be the one to reduce her working hours, because she tends to be the one who is earning less.

  And then there’s the cost of childcare. Recent research from the UK’s Department for Education found that 54% of mothers who don’t work outside the home said they would like to ‘if they could obtain convenient, reliable, and affordable childcare’.75 But on the whole, they can’t. Childcare costs in the UK have outstripped general inflation over the last ten to fifteen years,76 with UK parents spending 33% of their net household income on childcare against an OECD average of 13%.77 Unsurprisingly, therefore, the UK has highly unequal take-up of childcare by socio-economic levels, particularly compared to other OECD countries.78 And this also has a knock-on effect on female paid employment: 29% (this rose to nearly 50% of low- to middle-income mothers) of British women told McKinsey that ‘returning to work after having a child is not financially viable – twice the number of men who say the same thing’.79

  It was a similar story in New York which, in 2012, was found by Pew Research Center to be the most expensive state in the US for childcare.80 The Center for American Progress found that before the city’s mayor introduced universal preschool ‘more than one-third of New York families waitlisted for childcare assistance lost their jobs or were unable to work’. In Los Angeles, where preschools face steep funding cuts, an estimated 6,000 mothers are set to give up about 1.5 million work hours, costing an annual total of $24.9 million in lost wages.

  There is an easy fix to this problem. One study found that, with consistent childcare, mothers are twice as likely to keep their jobs. Another found that ‘government-funded preschool programs could increase the employment rate of mothers by 10 percent’.81 In 1997, the government of Quebec provided a natural experiment when they introduced a subsidy for childcare services. Following the introduction of the subsidy, childcare prices fell. By 2002 the paid-employment rate of mothers with at least one child aged 1-5 years had increased by 8% and their work hours had increased by 231 per year.82 Since then, several other studies have found that the public provision of childcare services is ‘strongly associated’ with higher rates of women’s paid employment.83

  Transferring childcare from a mainly unpaid feminised and invisible form of labour to the formal paid workplace is a virtuous circle: an increase of 300,000 more women with children under five working full-time would raise an estimated additional £1.5 billion in tax.84 The WBG estimates that the increased tax revenue (together with the reduced spending on social security benefits) would recoup between 95% and 89% of the annual childcare investment.85

  This is likely to be a conservative estimate, because it’s based on current wages – and like properly paid paternity leave, publicly funded childcare has also been shown to lower the gender pay gap. In Denmark where all children are entitled to a full-time childcare place from the age of twenty-six weeks to six years, the gender wage gap in 2012 was around 7%, and had been falling for years. In the US, where childcare is not publicly provided until age five in most places, the pay gap in 2012 was almost double this and has stalled.86

  We like to think that the unpaid work women do is just about individual women caring for their individual family members to their own individual benefit. It isn’t. Women’s unpaid work is work that society depends on, and it is work from which society as a whole benefits. When the government cuts public services that we all pay for with our taxes, demand for those services doesn’t suddenly cease. The work is simply transferred onto women, with all the attendant negative impacts on female paid labour-participation rates, and GDP. And so the unpaid work that women do isn’t simply a matter of ‘choice’. It is built into the system we have created – and it could just as easily be built out of it. We just need the will to start collecting the data, and then designing our economy around reality rather than a male-biased confection.

  CHAPTER 13

  From Purse to Wallet

  It was 11 p.m. on the evening of the UK’s 2017 general election. The polls had been closed for one hour, and a rumour had started doing the rounds on social media. You
th turnout had gone up. A lot. People were pretty excited about it. ‘My contacts are telling me that the turnout from 18-24 year olds will be around 72/73%! Finally the Youth have turnedddd out!! #GE2017’ tweeted1 Alex Cairns, CEO and founder of The Youth Vote – a campaign to engage young people in UK politics. A couple of hours later, Malia Bouattia, then president of the National Union of Students, put out the same statistic in a tweet that went on to be retweeted over 7,000 times.2 The following morning David Lammy, Labour MP for the London borough of Tottenham, tweeted his congratulations: ‘72% turnout for 18-25 year olds. Big up yourselves #GE2017’.3 His tweet received over 29,000 retweets and over 49,000 likes.

  There was just one problem: no one seemed to have the data to back any of this up. Not that this stopped news outlets from repeating the claims, all citing either unverified tweets or each other as sources.4 By Christmas Oxford English Dictionaries had named ‘youthquake’ as its word of the year, citing the moment ‘young voters almost carried the Labour Party to an unlikely victory’.5 We were witnessing the birth of a zombie stat.

  A zombie stat is a spurious statistic that just won’t die – in part because it feels intuitively right. In the case of the UK’s 2017 general election we needed an explanation for why, contrary to nearly all polling predictions, the Labour Party did so well. An unprecedented increase in youth turnout fitted the bill: Labour had courted the youth vote, the story went, and it had almost won. But then, in January 2018, new data emerged from the British Electoral Survey.6 There was some debate over how definitive the data was,7 but the famous youthquake was downgraded to more of a youth-tremor at best. By March no one credible was talking about a ‘youth surge’ without substantial caveats, and the 72% statistic was firmly on life support.8

  The British youthquake that never was had a fairly short life for a zombie stat. This is partly because while secret ballots preclude the possibility of absolutely conclusive polling data, we do at least collect data on them. A lot of data, in fact: elections are hardly an underresearched topic. But when a zombie stat emerges in an area where data is scarce, the stat becomes much harder to explode.

  Take the claim that ‘70% of those living in poverty are women.’ No one is quite sure where this statistic originated, but it’s usually traced to a 1995 UN Human Development Report, which included no citation for the claim.9 And it pops up everywhere, from newspaper articles, to charity and activist websites and press releases, to statements and reports from official bodies like the ILO and the OECD.10

  There have been efforts to kill it off. Duncan Green, author of From Poverty to Power, brands the statistic ‘dodgy’.11 Jon Greenberg, a staff writer for fact-checking website Politifact, claims, citing World Bank data,12 that ‘the poor are equally divided by gender’, with, if anything, men being slightly worse off. Caren Grown, senior director of Gender Global Practice at the World Bank, bluntly declares the claim to be ‘false,’ explaining that we lack the sex-specific data (not to mention a universally understood definition of what we mean by ‘poverty’) to be able to say one way or the other.13

  And this is the problem with all this debunking. The figure may be false. It may also be true. We currently have no way of knowing. The data Greenberg cites no doubt does indicate that poverty is a gender-blind condition, but the surveys he mentions, impressive though their sample size may be (‘a compilation of about 600 surveys across 73 countries’), are entirely inadequate to the task of determining the extent of feminised poverty. And having an accurate measure is important, because data determines how resources are allocated. Bad data leads to bad resource allocation. And the data we have at the moment is incredibly bad.

  Gendered poverty is currently determined14 by assessing the relative poverty of households where a man controls the resources (male-headed household) versus households where a woman controls the resources (female-headed household).15 There are two assumptions being made here. First, that household resources are shared equally between household members, with all household members enjoying the same standard of living. And second, that there is no difference between the sexes when it comes to how they allocate resources within their households. Both assumptions are shaky to say the least.

  Let’s start with the assumption that all members of a household enjoy an equal standard of living. Measuring poverty by household means that we lack individual level data, but in the late 1970s, the UK government inadvertently created a handy natural experiment that allowed researchers to test the assumption using a proxy measure.16 Until 1977, child benefit in Britain was mainly credited to the father in the form of a tax reduction on his salary. After 1977 this tax deduction was replaced by a cash payment to the mother, representing a substantial redistribution of income from men to women. If money were shared equally within households, this transfer of income ‘from wallet to purse’ should have had no impact on how the money was spent. But it did. Using the proxy measure of how much Britain was spending on clothes, the researchers found that following the policy change the country saw ‘a substantial increase in spending on women’s and children’s clothing, relative to men’s clothing’.

  Of course, 1977 was a long time ago, and you’d be forgiven for hoping things might have changed since then. Unfortunately, however, this is the most recent sex-disaggregated data we have for the UK, so it’s impossible to say. But we do have more recent data from other countries (including Ireland, Brazil, the US, France, Bangladesh and the Philippines) and it is not encouraging. Money continues not to be shared equally between couples, and money controlled by women continues to be more likely to be spent on children (a gender-neutral word which itself hides a wealth of inequalities17) than money controlled by men.18 So unless the UK is a secret feminist paradise (I can confirm that it is not), it’s safe to say that very little has changed.

  This being the case, the British government’s decision to introduce a new benefit called universal credit (UC) is unfortunate. UC merges several benefits and tax credits (including child tax credit) and, unlike the benefits it replaces, it is paid by default into the account of the main earner in each household.19 Given the gender pay gap, this is almost universally the man in heterosexual couples – and ‘almost universally’ is as exact as we’re going to get on this, because the UK’s Department for Work and Pensions isn’t collecting sex-disaggregated data on who the money is going to. So, in the UK at least, the data gap on gendered poverty is about to get even bigger.

  Now we’ve established that men and women have different spending priorities, it should be clear that there is a big question mark over the second assumption, that living in a male-headed versus a female-headed household has no implications for your standard of living. And this is indeed what the data we have shows. In Rwanda and Malawi, children from female-headed households were healthier than children from male-headed households – even when the male-headed households had higher incomes.20

  An analysis of the 2010 Karnataka Household Asset Survey in India was even more damning.21 When merely comparing female-headed to male-headed households, there was not much gender difference found in poverty levels. However, when poverty was assessed on an individual level, the difference was dramatic, with, wait for it, 71% of those living in poverty being women. And within those living in poverty it was women who experienced the greatest level of deprivation. Perhaps most damning for the validity of using household wealth to measure gendered poverty, the majority of poor women belonged to ‘non-poor’ households.

  It’s time for us to kill off the zombie assumptions that poverty can be determined at a household level, or that ‘female-headed’ has the same implications for male poverty that ‘male-headed’ has for female poverty. They are based on faulty data and non-gender-sensitive analysis. More than this, they add to and perpetuate the gender data gap. And they have led to some policy decisions that are disastrous for women.

  In the US, nearly all married couples file a joint tax return. They don’t have to: they have the choice of filing either
individually or as a couple. But the system incentivises them so strongly – through lower taxes and access to certain tax credits – to file jointly that 96% of married couples do.22 And the result, in practice, is that most married women in the US get over-taxed on their income.

  The US tax system is progressive, which means there are several tax bands. The first $10,000 or so that you earn gets taxed at a lower rate than the next $10,000 you earn, and so on. So, let’s say you earn $20,000 and your friend earns $60,000. For the first $20,000 of her income, you and your friend will pay the same amount of tax. But she will pay a higher rate of tax on the income she earns above that. That is, unless you happen to be married to that person and you file a joint tax return with her. In that case, you and your partner are treated as a single economic unit, with an income of $80,000, and how your tax is calculated changes.

  In a married couple’s joint tax return, the couple must ‘stack’ their wages. The higher earner (given the gender pay gap this is usually the man) is designated the ‘primary earner’, and their income occupies the lower tax bracket. The lower earner (usually the woman) becomes the ‘secondary earner’, and their income occupies the higher tax bracket. To return to our couple earning $60,000 and $20,000, the person earning $20,000 will be taxed on that income as if it is the final $20,000 of an $80,000 salary, rather than all she earns. That is, she will pay a much higher rate of tax on that income than if she filed independently of her higher-earning husband.

  Defenders of the married-couple tax return will point out that overall the couple is paying less tax by filing together. And this is true. But because, as we’ve seen, the assumption that household resources are shared equally is flawed to say the least, a couple paying less tax doesn’t necessarily translate into more money in the secondary earner’s pocket than if she’d filed individually. And this is before we even address any issues of how financial abuse may be making the joint filing system even worse for women. 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 disincentives married women from paid work altogether (which, as we have also seen, is bad for GDP).23