The FT’s Federica Cocco and John Burn-Murdoch examine how gender-biased data can mask important sex differences – with potentially-dangerous consequences. Leave your questions and suggestions in the comment section and Federica and John will answer them on camera in their next ‘Crunched’ episode. Read more of Federica and John’s data coverage:
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 counting creado perez which makes him i think a lot of strong points that averages aggregate statistics are you know understood by most people to be representative of the whole but they actually contain a lot of biases and
Especially around the topic of gender i’ve been looking at transport as a topic when you look beneath the surface the way that we the way that people travel around does actually have some quite distinct gender-based patterns so i’m gonna use some of our handy number blocks now to show the different gender patterns you get in transport so this is from some eurostat
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 percent 10 and then i’ve got a nine at the end travel by car on a typical day versus 49 percent 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 percent of man travel by public transport on a typical day versus 22 percent of women if we then look at walking around 11 percent of men walk on a typical day any substantial distance and 17 percent of women so road use through 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 is like is ice mainly roads and so when the government decides all right we’re going to build loads in new roads are going to plow loads of money into the roads what they’re
Doing is they’re essentially most of that money is going to man it’s making men’s daily lives easier most that money’s not going to women you know when you view that alongside the fact that over the last seven years the amount of money the government gives to local authorities spend on their bus routes has almost hard it’s gone from three hundred and seventy five
Million which i’m just going to write here so spending on buses has gone from three hundred and seventy five million in 2011 to two hundred million 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 we’re saying that this disproportionate allocation of resources is because a date we see the data is gender neutral whereas it isn’t exactly so that creates some inherent sexism right this is that this is the gender data gap so people here owe 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’s it relates to poverty and we look at poverty statistics you know will say example in the uk in roughly twenty two percent of the population is lives in poverty yeah and sometimes it’s broken down by age so we’ll know how many children live in poverty how many
Are dogs 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 the population that is poor men and what share the population is poor women let’s see some numbers okay 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 will start in 1994-95 and will end with the last financial year for which the data is available and will start from 25 to 43 so this is what happened with men we started out at just 29 percent 30 percent actually and then it went up and up financial
Crisis and then as gradually sort of going down and now we’re at about thirty wouldn’t close only 4% yeah women 94/95 started at 39% sort of remained flat went up a little bit but basically it’s been flats and it’s now where it started at 39% but consistently been higher than men so again i guess the issue here is when when we hear people talking about our poverty
As a problem we need to reduce poverty you know they’re not thinking about how poverty affects the genders differently also it brings a straight to the core of the gender data gap because the way that this is measured or a release away to the uk government measures poverty by gender is that it looks like at the head of the household and looks 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 instead you should be looking at what to measure poverty instead
You should be looking at individuals so this is an example of a gender date the gender data gap where apparently looks like were being neutral in the way that we measure numbers but actually were not taking into account very important gender dynamics that happen for example that the majority of the head of households are men yeah an even more shocking example of
The gender data gap can be seen when we look at health statistics so for many many years in the u.s. health tests were run with only male subjects this happened because of the to the divine scandal in the 60s when those of pregnant women were prescribed medicines for morning sickness that ended up causing loads of health effects on their babies so because of that
Tests were run solely on men including tests on 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 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 but what right men can have breast cancer but ovarian cancer i’ve never heard of an example but yeah you’re right so in a really good example of how this plays out the effects of these sort of 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 okay why is why would that be what’s 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
Used to assess you know what’s wrong with the patient patients especially those with acute coronary issues so issues relating to the heart were developed in patient populations where the patient’s involved two-thirds of them were men so you’ve got studies being done to see how should we deal with things like heart attacks where two-thirds 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 are presented very differently in men and women and so women women with heart attacks that heart attack is being spotted later if at all and then people are perhaps responding with treatments
That were specialized are men so yeah this has real outcomes that affect women much worse than that so we’ve looked at house 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 running where do you stop it’s
Important to look for example when will you look at the pay gap the ethnic pay gap is very important as well and also the class pay gap i think 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 like 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 and that 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 you decision-makers should at least be aware that when they’re looking 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 privileged in one group over another and even the ordinary
Neutral person when they see a statistic that supposedly represents an average they should think twice absolutely if you’d like us answer questions regarding this or other crunched episodes or have certain topics in mind that you’d like us to dissect feel free to leave suggestions in the comment section below and we’ll make sure that we’ll try and get to them in our next episode
Transcribed from video
Sexism and data: When statistics hurt women | Crunched By Financial Times
