Unpacking the gender data gap

(Jennifer Looi) #1

This International Women’s Day, I’ve been doing a lot of thinking about the gender data gap. For those who might be unfamiliar with the concept, the gender data gap is essentially the consequence of living in a male dominant/male-by-default society in which data with respect to women is not widely collected and/or dis-aggregated and given due diligence. By and large, technological advances are tested and designed based on “the average”, and in particular, the average male. This manifests in a lot of ways, with a frequently cited example being colder office temperatures - certainly an inconvenience, but not exactly life-threatening.

A new book that’s caught my eye, Invisible Women: Exposing Data Bias in a World Designed for Men, explores the real dangers associated with the gender data gap. In an article about the book in The Guardian, some of the most interesting examples are about how technology is by and large tested on men, and simply scaled down for women without significant regard for other biological differences. Car crash test dummies and safety wear don’t account for our different proportions making them ill-suited for determining how well we would actually be protected from harm; our thinner skin, higher body fat, and fluctuating hormones aren’t largely accounted for in chemical and biological testing on hazardous products.

The result? Women are significantly more likely to be injured in car accidents, and suffer from serious injuries at work due to poor fitting personal equipment. Not to mention the under-studied risks of “women’s work” at nail salons, etc. in which women are subjected to high degrees of exposure to hazardous chemicals. A simple comparison would be to an engineer working at a nuclear plant (commonly seen as men’s work), whose exposure to radioactivity is monitored, and accounted for in workplace safety: at unsafe levels of radioactivity, engineers move offsite.

The data gap is an issue that is getting increasing attention, including everyone from the federal government, to philanthropies like the Gates Foundation which wrote about the issue in their 2019 Annual Letter, to the World Bank. What all this means is that collecting and analyzing disaggregated data matters. The issues I’ve highlighted here are about women, but the data gap exists for plenty of other “minority” groups. As I move forward in my work, I know that I’ll be pushing myself to think harder about what it means to be inclusive, and who it is that’s being included.

*Note: I’ve used gender and sex interchangeably here to reflect the language of the dominant conversation about this, but recognize that these are distinct.

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