Do you have to be counted to count? How strengthening gender data can empower women and girls
Most of us are aware of the gaps that separate women from men, when it comes to opportunities, earnings or access to education. But not everyone realizes that even the data that we use to measure these inequalities have gender gaps. As the UN Statistical Commission holds its 50th session, we ask Francesca Grum, Chief of the Social and Gender Statistics Section at UN DESA’s Statistics Division, how gender data gaps are formed and what can be done about them.
Are women and girls visible in the data we use to make decisions?
“Women and girls, and men and boys, are becoming more visible in official statistics thanks to the growing demand for reliable, inclusive, disaggregated and open gender data. New methods help us better capture gender issues through population censuses, administrative records and surveys. This provides researchers and policymakers with evidence for gender analyses and allows to create and promote better policies on women and girls’ advancement and gender equality.”
“For example, women spend on average about three times as many hours in unpaid domestic and care work as men. Analyses of time use data with a gender lens may lay the foundation for policies that would recognize the tremendous value of unpaid work, reconcile paid and unpaid work and foster the ability of women and girls to engage in other activities, such as education. UN DESA’s Statistics Division is working to modernize time use surveys to improve the collection and use of time use data.”
“Gender statistics not only make women and girls and men and boys more visible, but they also help bust myths and stereotypes drawn with unfair social norms and attitudes.”
Why do we have such a large gender data gap and what can we do to close it?
“Gender data gaps persist due to lack of national capacity in producing and using gender statistics. Insufficient coordination among data producers and the dearth of financial resources are also major problems. However, thanks to improved and new data collection methods that better capture gender issues and by progressively eliminating gender bias in the existing data collection tools, we are narrowing the gaps.”
“For instance, if we use the household as a unit for data analyses and dissemination, we assume that there is homogeneity among all household members. However, this traditional approach fails to highlight potential disparities among the members of a household, let’s say, when measuring asset ownership. The Evidence and Data for Gender Equality (EDGE) project, implemented by UN DESA and UN Women, developed the UN Guidelines for Producing Statistics on Asset Ownership from a Gender Perspective to measure asset ownership at the individual level by using self-responses instead of proxies. The data analyses resulting from these guidelines are expected to shed light on policy issues around empowerment of women and their well-being, reduction of poverty and vulnerability and women’s entrepreneurship.
“Furthermore, the updated international classification of status in employment (ISCE-18) is a positive development that will result in better gender data, as it covers all forms of work, paid and unpaid, and additional details about types of employment, including those where women predominate, such as contributing family members. The International Labour Organization data collection guidelines for ISCE-18 are expected to further minimize possible biases in the instruments used to collect employment statistics. This way, questions will be asked in a way that should not elicit structurally different responses from women and men.
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