A sideways look at economics
Do we need to change the way men think? It’s a question that has echoed widely since the devastating news of missing Clapham woman Sarah Everard, whose remains have been found in a wood in Kent. Her fate has triggered a #MeToo response on social media, with many women breaking silence to describe for the first time their own experiences of abuse and feelings of unsafety.
Men have spoken up too, including this comment from Jay Rayner: “If I find myself walking behind a single woman on a quiet street I always cross the road to the other side and ideally accelerate away as quickly as possible. I am a big man and absolutely aware what my silhouette looks like in the darkness.”
Rayner has come in for some derision, but his remark demonstrates that he understands that personal safety is an issue that men and women experience differently on a routine basis. It is a point that is missed by the many commentators, both male and female, who simply repeat the crime statistics showing that young men are more likely to be the victims of street violence.
Personal safety is not the only issue where the routine, daily experiences of men and women tend to differ, often widely, but where the prevailing viewpoint of society is based around the experiences of men. The topic is taken up by the writer Caroline Criado Perez in her award-winning book, Invisible Women, where she describes a ‘one-size fits men’ approach, in which the needs and viewpoints of men take priority in everything from everyday designs and workplace policies to economic decisions. When this male-centric viewpoint is enshrined in public policy, the result is something of a ‘man’s world’.
Take the average smartphone, for example – at 5.5 inches it is too big for most women’s hands, and doesn’t tend to fit in our pockets either. Speech-recognition software is largely trained on recordings of male voices, with Google’s version 70% more likely to understand men as a consequence.
Most office thermostats are set five degrees too cold for women, Criado Perez says. That is because the formula to determine their temperature was developed in the 1960s and is based on the metabolic resting rate of an average 40-year-old, 70kg male. That overlooks the fact that women’s metabolisms are slower, leaving us cold, uncomfortable, and blanket-laden.
Issues like these, caused by overlooking or misusing data about the female experience — data that are freely available but which no-one thinks to consult — are often irritating, but not life-threatening. But when it comes to the collection of data, or rather the lack of data collection, where studies simply haven’t been carried out, the consequences can be much more severe, as Perez details.
For example, for many years, car safety tests were conducted on crash-test dummies the size and weight of an adult male in the driving seat. The result is that when female drivers are involved in an accident they are nearly 50% more likely to be seriously injured and 17% more likely to be killed than male drivers.
Similarly, women are under-represented in drug trials, their physiologies viewed by researchers as too complicated, too variable, too inconsequential or — at the other extreme — too much in need of protection (for fertility reasons, apparently). But with women’s bodies absorbing and metabolising drugs differently to men, this failure has resulted in drugs that would be beneficial to females being dropped prematurely during trial phases.
Another consequence is that suggested drug doses are not gender-specific. Based almost entirely on drug trials involving males or male cells, this results in women suffering more unexpected and more severe side effects than men. In these instances, the consequences of living in a world built around male data can be deadly.
It is not as if collecting separate data on women’s needs would be difficult. Data collection is increasingly ubiquitous. The advance of technology means that data on everyday activities like the number, length and asymmetry of our footsteps can all be recorded in real time. So too our geolocation, how many hours we sleep and even our web-browsing tendencies.
That wealth of information is used for a variety of purposes, such as targeted surveillance, ensuring lockdown compliance, personalised product ads, tailored insurance premiums, and assessments of creditworthiness, to name but a few. Why could it not also be used to help formulate public policy that takes account of the needs of women as well as men?
Yet apparently women are too complicated to properly capture even in the basic measure of economic activity, GDP. Despite recognising the value add of informal work, such as looking after elderly relatives — a role traditionally and still largely undertaken by females — it is considered too difficult to measure, so is not included at all. With women’s care-giving roles undervalued (since they don’t officially contribute to GDP), this has knock-on effects for economic policy, influencing what is considered a priority.
Perez cites the example of snow ploughing policy in Sweden. Prior to 2011, roads were cleared before pavements, as commuters using major traffic arteries were prioritised over pedestrians ferrying children or doing the shopping. But then officials realised that it’s easier to drive through three inches of snow than to push a buggy or wheelchair through it — notably tasks that women are more likely to undertake than men. Changing to clearing the pavements first turned out to have unexpected economic and health benefits, as pedestrians were three times more likely to be injured in icy conditions than car drivers.
What this particular example neatly highlights is that there are allocative efficiencies that can be gained from better monitoring, understanding, and use of gender-based data. Until then, countries risk allocating resources inefficiently, companies will struggle to get the most from 50% of their workforce (an uncomfortable workforce is an unproductive workforce), and women will continue to die needlessly.
As Nobel Prize winner Gary Becker argued in his book The Economics of Discrimination, market forces should help promote progress on this front. Indeed, if the market is at least somewhat competitive, then those holding onto the archaic ‘one-size fits men’ approach will eventually flounder, losing out to those which re-evaluate and in doing so improve productivity.
But while decision-making roles remain dominated by men (in 2015 there were more men called John leading FTSE 100 companies than women in these top roles), progress will remain painfully slow, with decisions made by men likely to cater to men. The problem is so engrained that even recruiting more women into these roles, in a bid to challenge the male default, isn’t straightforward.
That is the case for a variety of reasons, but I was surprised to learn that even algorithms – designed to simplify the recruitment process and strip out prejudices – are feared merely to reinforce the status quo, with male programmers (who dominate the sector) wittingly or unwittingly incorporating biases in the words the programme searches for. As a consequence, algorithms are at risk of being designed in a way that selects applicants with characteristics typically deemed relevant and appropriate by men. Even the wording of a job ad can negatively impact women’s likelihood to apply.
The answer it seems is to change the way that society thinks, so that a broader range of human experience is accounted for when setting economic policy. But in order to achieve that, it appears that the first thing that needs to be done is to alter the way that men think.
Appalling cases like that of Sarah Everard do at least start a debate where the need for change can be discussed. One small advance would be change the way that data on violence are collected. As Jackson Katz suggests in his Ted talk ‘Violence against Women: it’s a Men’s Issue’, rather than collect data on how many women are subjected to kidnapping, or rape, or murder, why not collect and publish statistics on the perpetrators of rape or murder or kidnapping? Otherwise women remain framed as perpetual passive victims of perpetrator-less crimes.
We must all be vocal in our distaste for the degradation and exploitation of women, or women will continue to be disadvantaged, to feel unsafe, and to be blamed for putting themselves in dangerous situations by simply walking home at 9 o’clock at night.
 These statistics are as a proportion of car crashes that women are involved in. In absolute terms, men are more likely to be involved in a car crash.
 According to Perez, women make up just 11% of software developers, 25% of Silicon Valley employees, and 7% of partners at venture capital firms.