For treatment of a risk to be effective, you have to really understand it and the impact it may have (Who? What? Where? How? When? Why?). You have to give it more than a passing thought, not just make an assumption that may later prove wrong.
In 2011 the Swedish town of Karlskoga, about 150 miles west of Stockholm, was reviewing all of its policies to eliminate gender discrimination. Someone jokingly said that at least that would not include snow-clearing, a comment that made them look at snow-clearing.
Gender-specific data relating to travel are not comprehensive, but where they do exist they are consistent and quite clear. Two-thirds of the people who use public transport in France are women; the figures for Chicago and Philadelphia are similar. Men tend to dominate the access to a car. In London women are three times more likely to be the one to take a child to school and 25% more likely to “trip-chain” – regularly make a series of small interconnected trips such as the school run, taking an elderly relative to the doctor, picking up groceries on the way home from work, etc. Trip-chain disparity between women and men is found equally across Europe.
In Karlskoga, as in most places with harsh winters, snow-clearing had focused on clearing roads for car traffic. They now realised that this involved an unconscious bias against women, so they decided to prioritise pedestrian walkways and cycle paths – it costs the same and after all, it’s easier to drive in three inches of snow than it is to push a buggy or a wheelchair.
There was also an unexpected economic benefit. Swedish hospital data gathered since the 1980s show that pedestrians are three times more likely than motorists to be injured in icy or snowy conditions; the data from one Swedish city showed that 69% of pedestrians injured in such conditions were women. The data from another area of Sweden estimated (probably conservatively) that the cost of winter injuries to pedestrians came to around £3.2m a year, around two to three times more than the cost of snow-clearing.
Clearing the icy fog from the data to understand the impact of a specific risk can thus be beneficial on several levels.
Data Source: Invisible Women, Caroline Criado Perez.