Get mad, then get even: Advocating for data equality
At Planalytics, we love data. We love collecting it, analysing it, visualising it, helping others to do the same, and advocating for more of all that! In the week we celebrated International Women’s Day, team member, Lucy Cooper, encountered two creative works exploring the fascinating topic of data and gender. In this journal article, Lucy shares her excitement for Invisible Women by Caroline Criado Perez and Tim Harford’s wonderful podcast, Cautionary Tales, both of which explore the subject of data in ways that made her laugh, cry, and shout out loud.
What everyone is getting this Christmas.
There is something supremely satisfying about knowing what you are going to buy everybody for Christmas, in March. However, so important is Caroline Criado Perez’s book, Invisible Women, I might not be able to wait that long to press a copy of it into the hands of everyone I know. Invisible Women is the latest book from award-winning speaker, writer, and campaigner, Perez, and it is a devastating read. In it, Perez brilliantly exposes the gender data gap - that gap in our knowledge that exists at the heart of what the inside cover of the book calls the ‘perpetual, systemic discrimination against women’ […] that has created a pervasive but invisible bias with a profound effect on women’s lives”. The catalogue of injustice starts from the preface. And gallops on from there.
“Most of recorded human history is one big data gap. Starting with the theory of Man the Hunter, the chroniclers of the past have left little space for women’s role in the evolution of humanity, whether cultural or biological. Instead, the lives of men have been taken to represent those of humans overall. When it comes to the lives of the other half of humanity, there is often nothing but silence. ”
Invisible Women brings data bias to stark life with rich case studies, stories, and new research from across the globe. Not all of the facts in the book are entirely unfamiliar to me. I’m aware that women work longer hours than men but are paid less, and are disproportionately affected by domestic violence. Perez’s skill is in layering and interconnecting facts, figures, stories, and insights to allow you to appreciate anew the scale, complexity, breadth, and depth of the harm that data bias brings.
As a planner, I was particularly fascinated to read all the ways in which urban and transport planning perpetuate data bias. Failing to account for women’s risk of being sexually assaulted in public spaces; failing to provide affordable, accessible public transport that enables ‘trip-chaining’; perpetuating city zoning patterns that segregate housing, work, shopping, and entertainment into separate and distant locations. These are just a few of the examples of the consequences of making decisions based on data where half the population are effectively missing. They come from the UK, Sweden, America, India, and Brazil. But it doesn’t take a huge leap of imagination to realise data bias is alive and kicking here in Aotearoa New Zealand.
Who is most likely to be inconvenienced by many New Zealand streets having only one pedestrian footpath? Who is disenfranchised by non-existent or poorly serviced public transport routes in the provinces? What are the consequences of locating our greenfield development some distance from commercial centres, and who is most likely to bear those consequences? These are just a couple of the questions I’ve started to ask myself since starting Perez’s book. The word ‘women’ might not be the only one in your answer, but Perez shows persuasively and dispassionately that it has to be in the answer somewhere.
Extract from an article exploring Vienna’s approach to parks planning. When it comes to creating gender-equality, Vienna is doing the work.
But, as heartbreaking as some of Perez’s examples of data bias are (female lack of access to clean water and toilets is particularly egregious), she is clear to show that solutions to what she coins ‘lazy unthinking’ exist. The gradual increase in the collection of data capturing women’s experience, preferences, and travel and work patterns is starting to yield tangible results, such as the emergence of gender-equal parks planning in Vienna and the reorganisation of the snow-clearing programme in Sweden. However, what is striking is that efforts to redress the harm of data bias such as these appear to be subject to much higher evidential and cost-benefit tests than status quo ‘solutions’ based on incomplete data.
A great example of this is public transport investment decisions. In 2016, the London bus network introduced the hopper fare allowing passengers to make unlimited journeys on London's bus and tram network for just £1.50 within one hour of touching in. Under the previous system, every time a user boarded a bus, they were charged for a new journey. The hopper fare was particularly helpful to women, as women are far more likely to travel by bus (it’s cheaper than private car ownership and perceived as child friendly) and ‘trip-chain’ - a travel pattern of several small interconnected trips. So far, so equitable. Zoom across the pond to Chicago, however, and it’s a different story. Despite a 2016 study confirming to city officials that Chicago’s transport system is biased against female travel patterns, they boxed on charging for transfers. These might sound like small hurts. But when you consider that women’s paid and unpaid work results in a longer working day than men’s; and that that work makes a significant input to GDP, the marginalisation of women’s travel is simply unjust, unjustified, and, to put it mildly, utterly maddening!
Perez’s narrative will leave you fascinated and alarmed, outraged and desperately sad. But also, I hope, galvanised. It transforms the silence of the lives of women into a sustained and impassioned roar. One reason that, for me, this book works as an urgent call to arms is Perez’s adeptness at pointing out in which professional, political, economic, and social spheres of influence data bias is perpetuated, to the point where I, for one, have a clear sense that I can do something about it.
As a planner and researcher, for example, I can be far more alert to what the available data isn’t telling me, and start to advocate for those gaps to be filled. Whose story, aspirations, and preferences aren’t being heard in the formulation of a policy, plan, or proposal? What inequities are we in danger of bedding in? What measures can we take to shine a light on ‘hidden figures’? And as a buyer of presents, I can make sure everyone in my orbit gets a copy of this book.
My next recommendation is economist Tim Harford’s brilliant podcast, Cautionary Tales. I think secretly I’m a bit of an ambulance chaser, making Cautionary Tales an ideal fit for my ears with its focus on ‘human error, tragic catastrophes and hilarious fiascos’. And it’s a real treat to be told a story by Tim Harford. There is something deliciously and subtly non-linear about the way he crafts his narrative, taking you on a journey that ebbs and flows but always reaches its destination leaving you both immensely satisfied and curious.
His recent episode, ‘Florence Nightingale and Her Geeks Declare War on Death’, makes a perfect double bill with Invisible Women. I had no idea that Florence Nightingale was the queen of data visualisation. And at a time in history when 50 pages of tabulated figures in single-spaced 5pt italicised Times New Roman was considered the only way to present data. (Oh, and that only men were considered qualified to present that data.)
Nightingale’s most famous data visualisation shows how many more English soldiers were dying of cholera and other preventable diseases (blue) than battle wounds (red) during the Crimean War. (Black is all other causes of death.) Each wedge represents a month. The rose chart on the right is from before sanitation measures were implemented in English army hospitals and camps during the war, and the chart on the left is after. The left chart shows how simple public health measures save lives (source - image and text: ScienceNews).
Nightingale perplexed and terrified the medical, political, and military establishment of the 1850s by making the outlandish claim that taking action based on evidence would ultimately lead to more effective action being taken. And her evidence was data. Through meticulous data collection in the field, she was able to show it was poor sanitation and not battle wounds killing most English soldiers during the Crimean War in the 1850s and that such deaths were avoidable. How? By simple (and cheap) sanitisation techniques like hand-washing (sound familiar?!).
Like Perez, Nightingale looked to see what stories were not being told, and committed to use data to unsilence disenfranchised voices. In Nightingale’s case, those voices were of the young English soldiers whose conditions at Scutari Hospital were so appalling and so dehumanising it led her to write in 1857:
“ It is as criminal to have a mortality of 17, 19 and 20 per 1,000 in the Line Artillery and Guards in England when that of civil life in towns is only 11 per 1,000 as it would be to take 1,100 men per [year] out upon Salisbury Plain and shoot them.”
Nightingale’s approach to data visualisation is not without controversy, as Tim Harford’s podcast explains. However, that controversy does nothing to diminish my admiration for a woman that would not be, could not be, invisible.
Thank you to Tim Harford and his wonderful regular newsletter for putting me onto Caroline Criado Perez’s book, Invisible Women, in the first place. Tim, if you are reading this, I am slowly making my way through your wunderlist, ‘What are the best books ever published in the history of the universe?’ and have to say I feel a lot brainier and infinitely more geeky than I did before I started!
If you’ve read Invisible Women or listened to Cautionary Tales, or have any suggested reading or listening for the Planalytics’ team, we would love to hear from you!