This book is almost at the centre of a Venn diagram of the kind of book I enjoy. It is a book about data, and how the way in which our society collects and categorises it tends to ignore women. And in a world of big data, where decisions are increasingly made based on analysis of data, this matters.
The very first chapter has an arresting example. Sweden implemented a policy in 2011 that all local policies had to be looked at through a gender based lens. One local official joked that at least snow-clearing couldn’t possibly be though of as a sexist policy. But to his chagrin, that comment started people wondering whether it was possible.
At the time, in line with most administrations, snow-clearing began with the major traffic arteries and ended with pedestrian walkways and bicycle paths. But this was affecting men and women differently, because men and women travel differently. We lack consistent, sex-disaggregated data from every country, but the data we do have makes it clear that women are invariably more likely than men to walk and take public transport….
…the apparently gender neutral snow clearing schedule was not in fact gender neutral at all, so the town councillors switched the order of snow-clearing to prioritise pedestrians and public transport users.
Interestingly after this switch, it turned out that overall the change saved money. Most winter injuries in Sweden are pedestrian injuries, caused by slipping on icy pavements. The cost of the injuries was much greater than the cost of the snow clearing that helped to prevent them. But until someone explicitly asked the question of whether a snow clearing policy could be gender biased, nobody noticed, as the data wasn’t obvious.
Criado Perez’s book involves a brisk review of all the many different ways in which inadequate data can give quite the wrong impression of the world, subdivided into Daily life, the Workplace, Design, The Doctor, Public life, and Emergencies. Every chapter is full of infuriating examples like the one above.
There is a full chapter describing the many different ways in which women are excluded from most public health trials of new drugs or treatments. One study showed that a complex kind of pacemaker had, based on clinical trials, the choice of whether it should be used had been calibrated for male hearts. When the female results were aggregated (all the individual trials had not included a statistically significant sample), they found that if the women’s use had been calibrated on women’s results, then there would likely have been a 76% reduction in heart failure for those women who didn’t qualify based on male results, but did qualify when women’s statistical outcomes had been considered.
Another infuriating story is the story of Viagra. A 2013 study of its effectiveness in relieving period pain – dysmenorrhea, which affects the daily life of around 20% of women. The initial trail showed ‘total pain relief over 4 consecutive hours’ with ‘no observed adverse effects’. But the trial had to be stopped because the funding ran out. The comments to the researcher, Dr Richard Legro, who led the study, suggested that reviewers did not see dysmenorrhea as a priority public health issue. Criado Perez points out
this may be where the data gap comes in: there simply isn’t much research done on dysmenorrhea, which makes it difficult for pharma companies to know exactly how much money could be made on such a drug – ad therefor makes it harder for them to decide to fund trials.
In a chapter entitled A Costless Resource to Exploit Criado Perez points out that the majority of economic measurements, including the gold standard of Gross Domestic Product (GDP) deliberately excludes most women’s household work. To get the full story on this one, you should read Marilyn Waring’s original book Counting for Nothing, which laid out this case in detail. But whether consciously or unconsciously, in developing the original GDP measurements in the 30s, most women’s unpaid work was excluded, and most men’s unpaid work was included. And not much has changed since. And of course the outworking of this lack of measurement is that women’s unpaid work tends to be seen as a costless resource to exploit. If countries rein in their spending by increasing the unpaid work required (largely of women) for example, by reducing subsidies for aged care, or child care, then the trade offs are not obvious, even when they exist.
I could describe the infuriating examples at length; but that would make you less likely to read this book and discover them for yourself. Instead, I’ll close with Criado Perez’s call to action:
- The female body is half of the population.Remember to accommodate or consider the female body, whether in car design to avoid crash injuries, medical devices, or the design of cities otherwise there are real consequences of discomfort, injury and death in a disproportion way for women
- Sexual violence against women is rarely measured systematically, and therefore solutions are not considered systematically. Improve the statistical measurement of all aspects of sexual violence, and the solutions will often suggest themselves.
- Measure all of women’s work – whether the work they do on subsistence farms in Africa, or the work they do in the home in rich countries. When it is not measured; it isn’t considered in so many aspects of life; from development economics, to political changes to the way in which transport is delivered in cities.
Too often planners, statisticians, and other professionals decide that women’s lives, bodies and activities are too complicated to measure.
In the information age, there is a lot of hubris that big data will create the answers to everything. But if big data is only measuring half the population, it will make things worse, not better. Read this book, and if you have any influence over data; its collection or its use; think honestly about whether you are considering women as much as men in the way you collect your data. And remember that the more often decision makers reflect the general population (whether by gender or other measures of diversity) the more likely it is that decisions will be good for that diverse population.
Thanks Jennifer
I am going to go and read this – sounds fascinating – helpful for my daughters degree majoring in Gender studies too !