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

by Caroline Criado Perez

  PART V

  Public Life

  CHAPTER 12

  A Costless Resource to Exploit

  ‘How much will it cost?’ This is the first question that any-one proposing a policy initiative must answer, swiftly followed by ‘Can we afford it?’ The answer to the first question will be fairly straightforward, but the answer to the second is a little trickier. It will depend on the current state of a country’s economy, and that figure is more subjective than many of us think.

  The standard measure of a country’s economy is gross domestic product (GDP) and if economics has a religion, then this is its god. It is compiled from data collected in a range of surveys and represents the total value of goods (how many shoes were manufactured) and services (how many meals were served at restaurants) a country produces. It also includes how much we all got paid and how much we (including governments and businesses) have all spent. It all sounds very scientific, but the truth is that GDP has a woman problem.

  The formulation of a country’s official GDP figure is an inherently subjective process, explains Diane Coyle, professor of economics at Manchester University. ‘A lot of people think that [GDP] is a real thing. But actually, it’s a confection, with lots of judgments that have gone into its definition. And a lot of uncertainty.’ Measuring GDP is, she says, ‘not like measuring how high the mountain is’. When you see headlines proclaiming that ‘GDP went up 0.3% this quarter’, she cautions, you should remember that that 0.3% ‘is dwarfed by the amount of uncertainty in the figures’.

  Compounding this uncertainty are glaring gaps in the data used to compile the figures. There are plenty of goods and services that GDP simply doesn’t account for. And the decision over which to include is somewhat arbitrary. Until the 1930s we didn’t really measure the economy with any seriousness. But that changed in the wake of the Great Depression. In order to address the economic meltdown, governments needed to know more precisely what was going on, and in 1934 a statistician called Simon Kuznets produced the United States’ first national accounts.1 This was the birth of GDP.

  Then the Second World War came along, and it was during this period, explains Coyle, that the frame we use now was established. It was designed to suit the needs of the war economy, she tells me. ‘The main aim was to understand how much output could be produced and what consumption needed to be sacrificed to make sure there was enough available to support the war effort.’ To do this they counted everything produced by government and businesses and so ‘what governments do and what businesses do came to be seen as the definition of the economy’. 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. ‘Everyone acknowledges that there is economic value in that work, it’s just not part of ‘the economy’,’ says Coyle.

  This was not a mere oversight: it was a deliberate decision, following a fairly vigorous debate. ‘The omission of unpaid services of housewives from national income computation distorts the picture’, wrote economist Paul Studenski in his classic 1958 text The Income of Nations. In principle, he concluded, ‘unpaid work in the home should be included in GDP’. But principles are man-made, and so ‘after a bit of to-ing and fro-ing’, and much debate over how you would measure and value unpaid household services ‘it was decided’, says Coyle, ‘that this would be too big a task in terms of collecting the data’.

  Like so many of the decisions to exclude women in the interests of simplicity, from architecture to medical research, this conclusion could only be reached in a culture that conceives of men as the default human and women as a niche aberration. To distort a reality you are supposedly trying to measure makes sense only if you don’t see women as essential. It makes sense only if you see women as an added extra, a complicating factor. It doesn’t make sense if you’re talking about half of the human race. It doesn’t make sense if you care about accurate data.

  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. ‘For example buying pre-prepared food from the supermarket rather than making it from scratch at home. Buying clothes rather than making clothes at home.’ Productivity hadn’t actually gone up. It had just shifted, from the invisibility of the feminised private sphere, to the sphere that counts: the male-dominated public sphere.

  The failure to measure unpaid household services is perhaps the greatest gender data gap of all. Estimates suggest that unpaid care work could account for up to 50% of GDP in high-income countries, and as much as 80% of GDP in low-income countries.2 If we factor this work into the equation, the UK’s GDP in 2016 was around $3.9 trillion3 (the World Bank’s official figure was $2.6 trillion4), and India’s 2016 GDP was around $3.7 trillion5 (compared to the World Bank’s figure of $2.3 trillion).

  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).6 In 2014 nearly 18 billion hours of unpaid care were provided to family members with Alzheimer’s (close to one in nine people aged sixty-five and older in the US are diagnosed with the disease). This work has an estimated value of $218 billion,7 or, as an Atlantic article put it, ‘nearly half the net value of Walmart’s 2013 sales’.8

  In 2015, unpaid care and domestic work in Mexico was valued at 21% – ‘higher than manufacturing, commerce, real estate, mining, construction and transportation and storage’.9 And an Australian study found that unpaid childcare should in fact be regarded as Australia’s largest industry generating (in 2011 terms) $345 billion, or ‘almost three times the financial and insurance services industry, the largest industry in the formal economy’.10 Financial and insurance services didn’t even make second place in this analysis; they were shunted into a lowly third place by ‘other unpaid household services’.

  You will notice that these are all estimates. They have to be, because no country is currently systematically collecting the data. And it’s not because there is no way of doing it. The most common way of measuring the amount of unpaid work women do is with time-use surveys. Individuals are asked to keep a time diary of their movements throughout the day – what they are doing, where, and with whom. It is because of this form of data capture, writes prize-winning economist Nancy Folbre, that we now know that ‘in virtually every country, women undertake a disproportionate share of all non-market work, and also tend to work longer hours overall than men do’.

  Standard time-use surveys were primarily designed to measure explicit activities such as meal preparation, house-cleaning or feeding a child.11 As a result, they often fail to capture on-call responsibilities, such as having to keep an eye on a sleeping child or be available for an adult with a serious illness while you get on with something else – another data gap. Time-use surveys that explicitly aim to capture such responsibilities demonstrate that the market value of ‘on-call care’, even at a very low replacement wage, is significant,12 but like with travel data this kind of care work is often lost within personal and leisure data.13 Folbre points to studies of home-based care for HIV/AIDS in Botswana which ‘estimated the value of services per caregiver at about $5,000 per year, a number that would substantially increase estimates of total spending on healthcare if it were included’.14

  The good news is that these surveys have been on the increase in many countries. ‘In the first decade of the 21st century, more than 87 such surveys were conducted, more than the total in the entire 20th century’, writes Folbre. But reliable time-use information is still lacking for many countries around the
world.15 And measuring women’s unpaid work is still seen by many as an optional extra:16 Australia’s scheduled 2013 time-use survey was cancelled, meaning that the most recent Australian data available is from 2006.17

  Coyle tells me that she ‘can’t help being a bit suspicious that the original decision not to bother counting work in the home was informed by gender stereotypes in the 1940s and 50s’. Her suspicion seems entirely justified, and not just because the original rationale for excluding women’s work was so flimsy. With the rise of digital public goods like Wikipedia and open-source software (which are displacing paid goods like encyclopaedias and expensive proprietary software), unpaid work is starting to be taken seriously as an economic force – one that should be measured and included in official figures. And what’s the difference between cooking a meal in the home and producing software in the home? The former has largely been done by women, and the latter is largely done by men.

  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.18 And so when countries try to rein in their spending it is often women who end up paying the price.

  Following the 2008 financial crash, the UK has seen a mass cutting exercise in public services. Between 2011 and 2014 children’s centre budgets were cut by £82 million and between 2010 and 2014, 285 children’s centres either merged or closed.19 Between 2010 and 2015 local-authority social-care budgets fell by £5 billion,20 social security has been frozen below inflation and restricted to a household maximum, and eligibility for a carers’ allowance depends on an earnings threshold that has not kept up with increases in the national minimum wage.21 Lots of lovely money-saving.

  The problem is, these cuts are not so much savings as a shifting of costs from the public sector onto women, because the work still needs to be done. By 2017 the Women’s Budget Group estimated22 that approximately one in ten people over the age of fifty in England (1.86 million) had unmet care needs as a result of public spending cuts. These needs have become, on the whole, the responsibility of women.

  Cuts have also contributed to a rise in female unemployment: by March 2012, two years into austerity, women’s unemployment had risen by 20% to 1.13 million, the highest figure for twenty-five years.23 Meanwhile, male unemployment stood at almost exactly where it had since the end of the recession in 2009. Unison found that by 2014 there had been a 74% increase in women’s underemployment.24

  In 2017 the House of Commons library published an analysis of the cumulative impact of the government’s ‘fiscal consolidation’ between 2010 and 2020. They found that 86% of cuts fell on women.25 Analysis by the Women’s Budget Group (WBG)26 found that tax and benefit changes since 2010 will have hit women’s incomes twice as hard as men’s by 2020.27 To add insult to injury, the latest changes are not only disproportionately penalising poor women (with single mothers and Asian women being the worst affected28), they are benefiting already rich men. According to WBG analysis, men in the richest 50% of households actually gained from tax and benefit changes since July 2015.29

  So why is the UK government enacting policy that is so manifestly unjust? The answer is simple: they aren’t looking at the data. Not only are they not quantifying women’s unpaid contribution to GDP, the UK government (like most governments worldwide) also aren’t gender-analysing their budgets.

  By repeatedly refusing (most recently in December 2017) to produce a comprehensive equality impact assessment of its budgets, the UK government has arguably been operating illegally since the public sector equality duty (PSED) came into law. Part of the 2010 Equality Act, the PSED requires that ‘a public authority, must, in the exercise of its functions, have due regard to the need to eliminate discrimination [and] advance equality of opportunity’.30 In an interview with the Guardian, WBG’s director, Eva Neitzert, couldn’t see how the Treasury could fulfil its legal obligations without completing a formal assessment.31 Were Treasury ministers ‘deliberately seeking to hide inconvenient truths about the impact of its policies on women?’ she wondered.

  If they were, it would be profoundly foolish, because spending cuts on public services are not just inequitable, they are counterproductive. Increasing the amount of unpaid work women have to do lowers their participation rate in the paid labour force. And women’s paid labour-force participation rate has a significant impact on GDP.

  Between 1970 and 2009, almost 38 million more women joined the US labour force, increasing the female participation rate from 37% to nearly 48%. McKinsey calculates that without this increase, US GDP would be 25% smaller – ‘an amount equal to the combined GDP of Illinois, California and New York’.32 The World Economic Forum (WEF) has also found that increasing female labour participation ‘has been an important driver of European economic growth in the last decade’. By contrast, ‘Asia and the Pacific reportedly loses US$42 billion to US$47 billion annually as a region because of women’s limited access to employment opportunities’.33

  There are still further gains that could be made. There is a 12% employment gap between men and women across the EU (the figure varies between 1.6% in Latvia and 27.7% in Malta);34 a 13% gap in the US;35 and a 27% gap worldwide.36 The WEF has calculated that closing this gap ‘would have massive economic implications for developed economies, boosting US GDP by as much as 9% and eurozone GDP by as much as 13%’.37 In 2015 McKinsey estimated that global GDP would grow by $12 trillion were women able to engage in the paid labour force at the same rate as men.38

  But they aren’t, because they simply don’t have the time. Both the OECD39 and McKinsey40 have uncovered a ‘strong negative correlation’ between time spent in unpaid care work and women’s paid labour-force participation rates. In the EU, 25% of women cite care work as their reason for not being in the paid labour force.41 This compares to 3% of men.

  In the UK, women with young children are employed for shorter hours than those without children, while for men it is the other way around.42 This matches the situation in Mexico where, in 2010, 46% of mothers of very young children were in paid employment compared to 55% of women in households without children. The figures for men were 99% and 96%, respectively. In the US, female paid employment is actually pretty high amongst younger women, but it sharply declines after motherhood, ‘which is being progressively delayed’.43

  The failure to collect data on women’s unpaid workload can also stymie development efforts. Mayra Buvinic, senior fellow at the UN Foundation, points to a history of initiatives in low-income countries littered with training programmes that have failed because they ‘have been built on the mistaken assumption that women have plenty of free time, backed by limited data on women’s time-intensive work schedules’.44 Women may sign up for these programmes, but if the initiatives don’t account for women’s childcare demands, women don’t complete them. And that’s development money down the drain – and more women’s economic potential wasted. In fact, the best job-creation programme could simply be the introduction of universal childcare in every country in the world.

  Of course, it’s not just childcare that affects female paid employment. Elder care also takes up significant amounts of women’s time, and demand is set to increase.45 Between 2013 and 2050, the global population aged sixty or over is projected to more than double.46 By 2020, for the first time in history, the number of people aged sixty and over will outnumber children younger than five.47 And along with getting older, the world is getting sicker. By 2014, nearly a quarter of the world’s disease burden was in people aged over sixty – most of it chronic.48 By 2030 an estimated 6 million older people in the UK (nearly 9% of the total population) will be living with a long-term illness.49 The EU has already passed this milestone: 10% of its population50 (around 50 million citizens51) are estimated to suffer from two or more chronic conditions. Most of them are sixty-five years and over.52 In the US, 80% of over-sixty-fives have at least one chronic condition, and 50% have at least two.53
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  All these care needs (the US has an unpaid labour force of 40 million providing care for sick and elderly relatives54) affect women’s ability to work. Female carers are almost seven times more likely than men to cut back from full-time to part-time work.55 US women aged between fifty-five and sixty-seven who care for their parents unpaid reduce their paid work hours by, on average, 41%,56 and 10% of US women caring for someone with dementia have lost job benefits.57 In the UK, 18% of women who care for someone with dementia have taken a leave of absence from work, and nearly 19% have had to quit work either to become a carer or because their care-giving duties became a priority, while 20% of female carers have gone from working full-time to part-time. This is the case for only 3% of male carers.58

  If governments want to tap the GDP source of women’s increased participation in paid labour it’s clear that they have to reduce women’s unpaid work: McKinsey found that a decrease in the time British women spend doing unpaid work from five to three hours correlated with a 10% increase in their paid labour-force participation.59 As we’ve seen, introducing properly paid maternity and paternity leave is an important step to achieving this, by increasing female paid employment and potentially even helping to close the gender pay gap60 – which is in itself a boon to GDP. The Institute for Women’s Policy Research has found that if women had been paid equally in 2016, the US economy would have produced $512.6 billion more in income – which is 2.8% of 2016’s GDP, and represents ‘approximately 16 times what the federal and state governments spent in fiscal year 2015 on Temporary Assistance to Needy Families’.61