When data is sexist | Crunched
The FT's Federica Cocco and John Burn-Murdoch examine how gender-biased data can mask important sex differences - with potentially dangerous consequences.
Produced by Juliet Riddell, filmed by Richard Topping and Petros Gioumpasis
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Statistics are sexist.
How dare you. Numbers are neutral.
I mean, I don't know about that. We've both been looking at this book, Invisible Women by Caroline Criado-Perez, which makes, I think, a lot of strong points that averages, aggregate statistics, are understood by most people to be representative of the whole. But they actually contain a lot of biases, especially around the topic of gender.
I've been looking at transport as a topic. When you look beneath the surface, the way that people travel around does actually have some quite distinct gender-based patterns. So I'm going to use some of our handy number blocks now to show the different gender patterns you get in transport.
So this is from some Euro stat data. So this is representative of the whole EU; that's the EU 28. They looked at the percentage of men and the percentage of women who travel around on a typical day using different forms of transport. They found that with cars, 59 per cent, ten and then I've got a nine at the end, travel by car on a typical day versus 49 per cent of women, quite a margin there.
They then looked at public transport. So this is including buses and the underground trams, ferries, that kind of stuff, and they found that 15 per cent of men travelled by public transport on a typical day versus 22 per cent of women. If we then look at walking around, 11 per cent of men walk on a typical day any substantial distance and 17 per cent of women.
So road use through private driving, through cars, is male dominated. Public transport is female dominated. Now, why is this an issue? Well, as highlighted in the book, this becomes an issue when people just think of transport as, oh, yes, it's mainly roads.
And so when the government decides, all right, we're going to build loads of new roads, we're going to pile loads of money into the roads, what they're doing is they're essentially most of that money is going to men. It's making men's daily lives easier. Most of that money is not going to women.
Now, when you view that alongside the fact that over the last seven years, the amount of money the government gives to local authorities to spend on their bus routes has almost halved, it's gone from 375m which I'm just going to write here. So spending on buses has gone from 375m in 2011 to 200m. So what you're looking at there is spending on male dominated forms of transport has continued to be strong, but these huge cuts have come to buses which are part of this form of transport that is predominantly used by women.
And we're saying that this disproportionate allocation of resources is because we see the data as gender neutral, whereas it isn't?
And so that creates some inherent sexism.
Right. This is the gender data gap. So people here, oh, more money for roads, less money for buses. And they think this is just a cars versus buses thing. Whereas embedded in that is a male versus female thing.
I can think of another example of a sort of gender data gap, and it relates to poverty. When we look at poverty statistics, we'll say for example, in the UK, I think roughly 22 per cent of the population lives in poverty. And sometimes it's broken down by age. So we'll know how many children live in poverty, how many adults, and how many pensioners. And those are considered the most important statistics when it comes to poverty. But then, when you dig in the data, you see that there is a considerable difference between what share of the population that is poor are men and what share of the population is poor women.
Let's see some numbers.
OK. So these are poverty statistics from the UK's Department of Work and Pensions. We're looking at the percentage of the population that is poor that is men and women. We'll start in 1994, '95, and we'll end with the last financial year for which the data is available. And we'll start from 25 to 43.
So this is what happened with men. We started out at just 29 per cent, 30 per cent actually, and then it went up and up, financial crisis, and then it's gradually going down. And now we're at about 30, what is it?
34 per cent yeah. For women, in '94, '95, it started at 39 per cent. It sort of remained flat, went up a little bit. But basically, it's been flat, and it's now where it started at 39 per cent but it's consistently been higher than men.
So again, I guess the issue here is when we hear people talking about, oh, poverty is a problem, we need to reduce poverty, they're not thinking about how poverty affects the genders differently.
Also, it brings us straight to the core of the gender data gap because the way that this is measured or at least the way that the UK government measures poverty by gender, is that it looks at the head of the household and look at that breakdown by household. But we know from separate statistics, collected by a different government in India, that in that case, the majority of poor women live in households that are not considered poor. So that's because of very complex gender dynamic. However, it does show that it's not necessarily an adequate form to measure poverty. Instead, you should be looking at individuals.
So this is an example of the gender data gap where apparently it looks like we're being neutral in the way that we measure numbers, but actually we're not taking into account very important gender dynamics that happen, for example, that the majority of the head of households are men.
And even a more shocking example of the gender data gap can be seen when we look at health statistics. So for many, many years in the US, health tests were run with only male subjects. This happened because of the thalidomide scandal in the '60s, when loads of pregnant women were prescribed medicines for morning sickness that ended up causing loads of health defects on their babies. So because of that, tests were run solely on men, including tests for ovarian and breast cancer.
Now, this was eventually worked out in the '90s, but still, to this day, many academic tests are only run on male subjects or the gender breakdown is not provided. So this means that we will get a lot of health advice that tends to be "gender neutral" but doesn't look at how male and female bodies are different. And you were citing one earlier that was really interesting.
Yeah. I'm also just curious about how you assess ovarian cancer in men.
Right. Men can have breast cancer, but ovarian cancer, I've never heard of an example.
Interesting. But yeah, you're right. So a really good example of how this plays out, the effects of these male dominated studies, is that in 2016, the British Medical Journal found that young women were almost twice as likely as men to die in hospital. Now, that obviously begs the question of, OK, why would that be? What's happening there? And a big factor is the fact that separately a study by the American Heart Association found that several risk prediction models that doctors and hospitals use to assess what's wrong with the patient, especially those with acute coronary issues, so issues relating to the heart, were developed in patient populations where the patients involved, 2/3 of them were men.
So you've got studies being done to see how should we deal with things like heart attacks, where 2/3 of the subjects are men. So the average of that is obviously going to be skewed towards men, and it leads to situations such as those discussed in the book, where often the symptoms for a heart attack present very definitely in men and women, and so women with heart attacks, that heart attack is being spotted later, if at all, and then people are perhaps responding with treatments that were specialised around men. So yeah, this has real outcomes that affect women much worse than men.
So we've looked at health. We've looked at a key economic metric, and we've looked at transport. I think, in a way, we have proven that, yes, data can be sexist in all walks of life. Now, the thing many critics could say, well, if you break down data, where do you stop? For example, when you look at the pay gap, the ethnic pay gap is very important as well and also the class pay gap. You could go on forever. Where do you stop?
Right, and I guess especially with the ethnic pay gap, even when we break out, for example, black, Asian, and minority ethnic groups, that's still a single number that covers people with a hell of a lot of different experiences.
And it doesn't necessarily break it down by gender in itself. So for example, looking at Asian men versus Asian women, yes, you could go on forever. Maybe you should in some ways, but until you get to a sample size that is too small.
Sure. But then, I guess, what we're also saying is even if you're not going to break the data down to that fine detailed level, decision makers should at least be aware that when they're looking at an average for a large group, they need to be aware that a lot of people's lived experience, a lot of groups' lived experience, is very different to that, and they need to know that a single treatment might be privileging one group over another.
And even the ordinary neutral person, when they see a statistic that supposedly represents an average, they should think twice.