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

to just select ‘domestic duties’ because that describes the majority of what they do.¹⁶ This gap is then compounded by the tendency to ‘emphasize incomegenerating activities’, the result being that they often underestimate (often female-dominated) subsistence production. The censuses also tend to define agriculture as ‘field work’, which leads to an undercounting of the women’s work ‘such as rearing small livestock, kitchen gardening, and post-harvest processing’. It’s a fairly clear example of male bias leading to a substantial gender data gap.

A similar problem arises with the division of work by researchers into ‘primary’ and ‘secondary’ activities. For a start, secondary activities are not always collected by surveys. Even when they are, they aren’t always counted in labour-force figures, and this is a male bias that makes women’s paid work invisible.¹⁷ Women will often list their paid work as their secondary activity, simply because their unpaid work takes up so much time, but that doesn’t mean that they aren’t spending a substantial proportion of their day on paid work. The result is that labour-force statistics often sport a substantial gender data gap.¹⁸

This male bias is present in the data Doss uses to check the 60-80% statistics. Foss concludes that women make up less than half of the global agricultural labour force, but in the FAO data she uses, ‘an individual is reported as being in the agricultural labor force if he or she reports that agriculture is his or her main economic activity’. Which, as we’ve seen, is to exclude a substantial chunk of women’s paid labour. To be fair to Doss, she does acknowledge the issues associated with this approach, critiquing the absurdly low 16% reported share of the agricultural labour force for women in Latin America. Rural women in Latin America, notes Doss, ‘are likely to reply that “their home” is their primary responsibility, even if they are heavily engaged in agriculture’.

But even if we were to address all these gender data gaps in calculating female agricultural labour we still wouldn’t know exactly how much of the food on your table is produced by women. And this is because female input doesn’t equal male output: women on the whole are less productive in agriculture than men. This doesn’t mean that they don’t work as hard. It means that for the work that they do, they produce less, because agriculture (from tools to scientific research, to development initiatives) has been designed around the needs of men. In fact, writes Doss, given women’s various constraints (lack of access to land, credit and new technologies as well as their unpaid work responsibilities) ‘it would be surprising if they were able to produce over half of food crops’.

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%.¹⁹ But they don’t. In an echo of the introduction of the plough, some modern ‘labour-saving’ devices might more precisely be labelled ‘male labour-saving’ devices. A 2014 study in Syria, for example, found while the introduction of mechanisation in farming did reduce demand for male labour, freeing men up to ‘pursue better-paying opportunities outside of agriculture’, it actually increased demand ‘for women’s labour-intensive tasks such as transplanting, weeding, harvesting and processing’.²⁰ Conversely, when some agricultural tasks were mechanised in Turkey, women’s participation in the agricultural labour force decreased, ‘because of men’s appropriation of machinery’, and because women were reluctant to adopt it. This was in part due to lack of education and sociocultural norms, but also ‘because the machinery was not designed for use by women’.²¹

It’s not just physical tools that can benefit men at the expense of women. Take what are called ‘extension services’ (educational programmes designed to teach farmers science-based practice so they can be more productive). Historically, extension services have not been female-friendly. According to a 1988-9 FAO survey (limited to those countries that actually had sex-disaggregated data) only 5% of all extension services were directed towards women.²² And while things have slightly improved since then,²³ there are still plenty of contemporary examples of development initiatives that forget to include women²⁴ – and therefore at best don’t help, and at worst actively disadvantage them.

A 2015 analysis by Data2x (a UN-backed organisation set up by Hillary Clinton that is lobbying to close the global gender data gap) found that many interventions simply don’t reach women in part because women are already overworked and don’t have time to spare for educational initiatives, no matter how beneficial they may end up being.²⁵ Development planners also have to factor in women’s (lack of) mobility, in part because of their care responsibilities, but also because they are less likely to have access to transport and often face barriers to travelling alone.

Then there’s the language and literacy barrier: many programmes are conducted in the national language, which women are less likely than men to have been taught. Due to the low global levels of female education, women are also less likely to be able to read, so written materials don’t help either. These are all fairly basic concerns and shouldn’t be hard to account for, but there is plenty of evidence that they continue to be ignored.²⁶

Many development initiatives exclude women by requiring a minimum land size, or that the person who attends the training is the head of a farming household, or the owner of the land that is farmed. Others exclude women by focusing solely on farms that have enough money to be able to purchase technology, for example. These conditions are all biased towards male farmers because women dominate the ranks of poor farmers, they dominate the ranks of small-scale farmers, and they are overwhelmingly unlikely to own the land that they farm.²⁷

In order to design interventions that actually help women, first we need the data. But it sometimes feels like we’re not even trying to collect it. A 2012 Gates Foundation document tells the story of an unnamed organisation that aimed to breed and distribute improved varieties of staple crops.²⁸