The Sustainable Development Goals (SDGs) will not be achieved without closing the gender data gap, said experts at the second UN World Data Forum.
When businesses, governments and other players are armed with data, they are able to improve the everyday decisions they make, yet for women and girls, basic information about their lives is lacking. As a result, their needs are not being prioritised and their contributions are undervalued, according to gender and data experts at the forum held in Dubai. “There is no equality of information on women and girls that is governing policy. Data in all aspects of girls’ and women’s lives is missing or is biased,” says Emily Courey Pryor, executive director of Data2X, an alliance dedicated to improving the quality and use of gender data that is housed at the United Nations Foundation.
Globally, unpaid work such as cooking, cleaning, as well as child and elderly care is mainly done by women, who spend two to 10 times more time on unpaid care work than men, according to a 2014 Organisation for Economic Co-operation and Development study.
An audit by UN Women shows that about a fifth or 53 out of 241 SDGs indicators explicitly refer to sex, gender, women and girls or are largely targeted at women and girls. A less restrictive criterion, where all indicators that are relevant for women and girls and can be disaggregated by sex are included, would yield a greater number of indicators.
But a third of the indicators proposed to track progress on gender issues cannot be generated globally because international standards measuring them do not exist and most countries do not conduct regular surveys on them, says Mayra Buvinic, a senior fellow at Data2X and Centre for Global Development.
How much does unpaid work by women contribute to Kenya’s economy? What explains gender wage gaps? How much of Kenya’s food production is by women? Good data can provide answers to these questions but the information is often not available.
Buvinic says several factors contribute to the problems of gender data. In some instances, there is no data on a particular issue, for example, on unpaid and voluntary work. Other times, data exists but it is not sex-disaggregated. There are also cases in which finding the right methodology for collecting and analysing data is difficult. “When someone farms their land for own use but also sells some of the produce in the market, how do you capture the value of the work?” asks Pryor.
How the data is measured matters and gender biases impede and distort data collection. A good example is household surveys where a household is viewed as a single unit without differentiating how various outc
omes, e.g. income, education and food insecurity, affect members of the family differently.
Buvinic explains that the survey applies the proxy respondent method, where one respondent reports on the activities and properties of the entire household. In male-headed households, the men usually answer for the whole family including the women yet the perspectives of men and women are different with regard to certain factors.
Biased data leads to conclusions that do not reflect people’s reality and view of the world, a situation that causes mistrust. For instance, in May this year, when the government released the 2015/2016 Labour Force Basic Report, indicating that Kenya’s unemployment rate was 7.3 percent, most people, including much of the public, economists, labour experts and the media questioned the figure because it was a drastic drop from the rate that had been quoted in official government and UN agencies documents until then. The government attributed the steep decline partly to the survey’s strict definition of the unemployment rate as people who are not working but are available and looking for work. This means that those who had given up on searching for work or were unavailable were not included. But still Kenyans did not buy the explanation.
A better way would be to measure how people experience employment, says Global Partnerships for Sustainable Development executive director Claire Melamed. “People don’t trust you when you tell them the economy is doing great yet they are employed on contracts, wages are stagnant and jobs are insecure,” she says. The partnership also runs a project in Kenya.
“We have no data or have bad data on issues that disproportionately affect women and girls but are not highly valued by the society,” says Buvinic.
A look at how Mrs Carol Ochieng, a mother of five children age six to 14, spends her day, highlights many of the challenges with unpaid care work data.
Living in Nyakach Constituency, Kisumu County, Carol toils for long hours daily, taking care of her family but her contribution to her community is cursorily captured under economically inactive people where homemakers are included in government labour and employment reports.
Routinely, Carol, like many rural women, wakes up at around 6am, makes porridge or tea for her children, as they head for school, she leaves to go fetch water for domestic use some distance from her homestead
Figures from the 2014 Kenya Demographic and Health Survey (KDHS) show that 40 percent of rural and 11 percent of urban households spend half an hour or longer to fetch water for home use.
Depending on the season, on her return, she either goes to till or weed her five-acre shamba or looks for menial work in other farms from which she earns Sh300 a day or goes to the forest to split wood for firewood to use at home and to sell at the market. Her two teen daughters help fetch the firewood out of school hours.
Her husband, a clerk in a local primary school, participates in the initial ploughing of the farm in preparation for planting seeds but much of the weeding and harvesting is left to her, with the older children helping at the weekends. She farms maize, millet, sweet potatoes, cassava, beans, groundnuts and traditional vegetables. When the harvest is good she sells some of the produce in the local market to earn money to supplement her husband’s income.
Typically, Carol returns home before 1pm to prepare lunch for her children, then begins the routine of preparing supper, which includes going to the market to buy food.
Carol’s experience and the low value she attaches to her otherwise massive role is not only typical in rural Kenya but all too familiar to many women around the world regardless of whether they are in full employment or not.
Globally, unpaid work such as cooking, cleaning, as well as child and elderly care is mainly done by women, who spend two to 10 times more time on unpaid care work than men, according to a 2014 Organisation for Economic Co-operation and Development (OECD) study. Women spend, on average, between three and six hours on unpaid care activities, while men spend between half an hour and two hours.
According to the labour report, the second main reason Kenyans age 15 years to 64 give for being “economically inactive” is being a housewife or family responsibility.
Buvinic says many women are left out in surveys because they consider themselves primarily as housewives, when in reality they, like Carol, work on farms, do seasonal work, run their own businesses, or do part-time jobs.
But with less than one-third of countries currently producing sex-disaggregated statistics on unpaid work and informal employment, who can blame women for underrating their worth?
According to a UN Statistics Division survey of 126 countries, only 42 percent regularly produce statistics on unpaid care work. Kenya is not one of them.
Systematically discounting unpaid work misrepresents reality in such a way as to make women appear more dependent and less productive than they actually are. It also leads to ineffective policies on gender inequalities, employment and other empowerment areas.
In the 1990s, Uganda did two surveys in which the question about labour force participation was framed differently. One year, the survey asked people only for their "primary" activity or job. In the next survey they added a new question, asking people if they had a "secondary" activity. The labour force participation increased by almost 10 percentage points from 78 percent to 87 percent. The additional workers – 702,000 – were mostly women who went unrecognised in the first survey.
But attitudes are changing, and some countries now administer time-use surveys. Estimates differ among countries that are attempting to measure the value of unpaid care work, from 20 percent to 60 percent of gross domestic product, according to the 2015 UN Human Development Report .
In her remarks at the start of the data forum in October this year, the UN Deputy Secretary-General Amina J. Mohammed said that with accurate, inclusive and disaggregated data, we can understand the challenges we face and identify the most appropriate solutions for sustainable development.
