Caroline Criado Perez’s book focuses on the challenges facing women in a world where the primary gatherers of data have been/usually still are men. It highlights the problem of data heavy systems not being as fair as the idea of impartial ‘math’ might imply. (See also here). The problem of what Perez calls the data gender gap.
Interesting, If Depressing, Issues To Consider
The book has a bunch of interesting, and often pretty depressing, stories of problems from round the world. Some have some pretty simple things that we can do. Who knows how much of the problem will be solved by changing the way we speak? Still, why not improve our language use? It surely can’t hurt.
Perez’s central theme is The Data Gender Gap. She argues that, because of this gap, often views of women aren’t considered and so don’t impact policy. E.g., snow clearing in a Swedish town was designed to aid commuting and largely benefited men. Yet the way it was designed didn’t help most women’s travel patterns.
The Data Gender Gap And Motivation Gender Gap
One problem is that there is clearly both a data gender gap and, what might be called, a motivation gender gap. These are pretty hard to disentangle. There is a conceptual difference between not knowing — we didn’t collect the data — and not caring — we have the data but can’t be bothered to use it. Motivation drives data collection. As such, they can have similar roots. Many of the problems do fall significantly into the motivation category. “…collecting the data is useless unless governments use it. And they don’t”. (Perez, 2019, page 77). The challenge is to get the data, and then also to act upon it.
The challenge of paternity leave for academics is highlighted. This is an interesting one. The argument is that men tend to benefit relatively from this when going up for tenure. They have extra time added to their clocks. Women get the same time added to their tenure clocks but the experience of parenthood tends to be much more burdensome for women. Consider the most common type of couple, in it even the most enlightened man doesn’t get pregnant or breastfeed. Well-meaning gender-equal policies thus can have a non-gender equal effect. This seems pretty clear to me. Of course, it is a challenge to know how to solve this. There will be atypical cases where men do end up bearing a very large responsibility. How should these factor into policy? Designing the right public policy can be hard but it doesn’t mean that we shouldn’t be trying to do better.
We Need Better Data
At the risk of seeming too predictable, I want to end by echoing the need for better data. What we should never do is to design policies where “there is ‘no way of judging whether they’re successful or worth mimicking, because there are no success metrics attached to any of them'” (Perez, 2019, page 109).
Read: Caroline Criado Perez (2019) Invisible Women: Data Bias in a World Designed for Men, Abrams Press, New York
