Women’s Unpaid Work Must Be Included In GDP Calculations: Lessons From History
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Their method was intended to provide a comprehensive and up-to-date picture of an entire national economy, by estimating the monetary value of all “economic” production that took place in a country in a given year. Like most economic statisticians of the day, Meade and Stone focused almost entirely on measuring the value of goods and services that were actually bought and sold.
But a problem quickly emerged, thanks to the experiences and observations of a 23-year-old woman named Phyllis Deane. She was hired by Meade and Stone in 1941 to apply their method in a few British colonies. In present-day Malawi and Zambia, Deane realised that it was an error to exclude unpaid household labour from GDP.
In a research paper I published recently on the history of the GDP, I write that Deane believed this convention excluded a great share of productive activity – especially in rural Africa. She argued that it was “illogical” to exclude the economic value of preparing and cooking food and collecting firewood. She contended that such kinds of labour had historically been excluded because they were commonly viewed as women’s work.
To decide which activities to include in her GDP calculations, Deane spent months conducting village surveys in order to measure, and include in GDP estimates, particularly burdensome activities like the collection of firewood.
She concluded that if governments wanted to formulate policies that increased aggregate national income and ensured an equitable distribution of that aggregate, the contributions of all producers – including rural women – had to be counted.
Over the next seven decades, GDP calculations would not generally include unpaid (and mostly female) labour. But Deane’s work shows us this was not the only way to measure economic production. As GDP calculations come under increasing criticism, we should look to her research for a way forward.
Invisibility of female labour
Richard Stone paid little attention to Deane’s recommendations. In 1953, he oversaw the publication of the United Nations’ first System of National Accounts. This report provided detailed standards for calculating GDP.
The system ignored Deane’s call to include unpaid household labour. And because UN technical assistance programmes sought to ensure that low and middle-income countries followed the system’s standards, Stone’s method had global consequences. Activities which were central to every day life in low-income African countries – like fetching water, grinding corn, and weaving mats – were not included in national accounts.
This invisibility of female labour in national income accounting eventually provoked a backlash. While pushing for female domestic labour to be economically quantified, scholar-activists like the Italian-born philosopher Silvia Federici, who taught for many years in Nigeria, argued that male “economic” production was impossible without women’s uncompensated “non-economic” labour.
For instance, without a wife to tend to the children and the home, how would a male factory labourer have the time or the energy to fulfil his stereotypical role as the breadwinner?
Time rather than money
Some feminist economists held a different view. In 1999 the New Zealand-born economist Marilyn Waring articulated concerns about including unpaid labour in national accounts. Rather than using economic activity to measure the value of labour, Waring called for a different indicator: time.
Time, she explained, was “the one investment we all have to make”. Drawing on research she conducted in rural Kenya, she argued that time-use surveys would demonstrate “which sex gets the menial, boring, low-status, and unpaid invisible work”.
Such surveys would show how targeted interventions, like access to clean water and efficient cooking stoves, could alleviate the drudgery of domestic labour and allow billions of women to gain greater freedom in how they spend their days.
In 2008, the authors of the newly updated System of National Accounts responded to their feminist critics by way of a compromise. They agreed to include the production of all goods – whether these were sold or not – in GDP calculations, so activities like weaving mats or brewing beer would be included.
However, they continued to exclude most unpaid household services, like cooking and cleaning. And the revised system ignored both Deane’s and Waring’s calls for more data on the distribution of time-use by gender. This has caused ever more criticism to be levelled at the system.
In recent decades, the work of feminist economics has shown how the methods of calculating GDP render much of women’s labour invisible. Meanwhile, surveys and time-use studies show the toll this has taken on women’s lives, particularly in the Global South. One recent report found that hundreds of millions of women worldwide have to walk more than a 30-minute round-trip to reach clean water for their families.
Future of the GDP
A 2009 report commissioned by then French President Nicolas Sarkozy stated that because GDP is “treated as a measure of economic well-being” it “can lead to misleading indicators about how well-off people are and entail the wrong policy decisions”.
More recently, the World Bank pointed out that GDP only measures flows of income but doesn’t tell us whether health care, education, and the wealth of the natural world are being built up or plundered. The Economist called for a “new metric” of economic progress that included “unpaid work in the home, such as caring for relatives”.
None of these insights are new. But they do mark a renewed appreciation for the economic indices and policies that feminist scholars have long favoured. For instance, Silvia Federici’s insistence that household labour should be paid has been at least partially realised in the spread of cash transfer programmes across Africa.
If we want to really bring women’s work out of the shadows and overturn the stereotypical gender roles that relegate women to more than their fair share of household labour, we must first take the blinders of the GDP.
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