A Riskier Life Due to the Gender Data Gap

The recent debacle of NASA canceling the all-female spacewalk (citing a lack of space suits of the right size for women) is proof enough of the existing gender data gap in a male-dominated world. In the past, NASA only had all-male or male-female missions. Now imagine one of the most anticipated NASA missions failing only because of a shortage of spacesuits that fit women. The gravity of this issue makes it one requiring thorough discussion. Spreading awareness about the #genderdatagap is profoundly necessary, which is why we want to share some details with you about one of the most read books on this topic at the moment – already a bestseller in the UK – Invisible Women: Exposing Data Bias in A World Designed for Men written by Caroline Criada Perez. With the help of case studies, interviews, and research, she illustrates how we could begin closing the gender data gap.

The Gender Data Gap

Most of us are not aware that the majority of data in the world is based on studies of the ‘default male’ (as the author likes to put it). Women are omitted from most research or studies resulting in a gender data gap. One example is in economic data – where women’s workplace care is not included. Medical data that includes cellular research is based mostly on the typical male. Symptoms of heart attacks differ between male and females, due to which women often get improper treatment. Drugs respond differently on a woman’s body than on a man’s. Crash test dummies are mostly of male proportions. Then there’s a double data gap when it comes to white females and women of color. The list of this gender data gap goes on and on. From economic development, healthcare, education, and public policy to governance, the gender data gap exists and it’s definitely not a good sign. Women often have to pay the hefty price of time, energy, and sometimes their own lives.

Examples of the Gender Data Gap

8000 people die in the UK every year due to work-related cancers. Over the past 50 years, breast cancer has increased significantly in the industrialized world. Researchers often know the diseases caused by dust among miners, but they don’t have the same data when it comes to women who are exposed to dust or chemicals. It’s startling to consider that occupational research still fails to collect that kind of data related to women. In hair and nail salons, the mostly female staff is exposed to harmful chemicals in the form of hair dyes, sprays, gels, removers, adhesives, etc. Studies show that these chemicals are linked to lung diseases, miscarriages, and cancer. Some alter the female body’s normal hormones and cause hormonal imbalances which further pave way for other diseases.

It’s generally believed that men drive cell phone purchases, yet research shows women are more likely to own an iPhone than men. Having said this, cell phones fit perfectly in the average male hand, but for a woman it’s proportionally too big. Additionally, the voice recognition software used by these phones is often male biased. A University of Washington study revealed that Google’s speech recognition software was 70% more likely to accurately identify male speech. Women pay the same price for gadgets like smart phones, iPads, or even car speech recognition systems and get inferior service (when it comes to voice recognition) or dexterity.

Closing the Gender Data Gap

To close the gender data gap, the first step is collecting the data. Without extensive data, it’s impossible to recognize where it’s lacking or the costs involved in bridging the gap. But if the data collected is carefully studied and planned, the gender data gap could be made smaller, which would then directly result in women boosting the economy, thus increasing a nation’s GDP. The existing gender inequality can only be minimized if this data gap is simultaneously closed. Research and studies should now include female prototypes as well to get fair results. Cultural or biological, the typical male has been used to represent the population of the world, which now has to be stopped or changed. Women deal with the bias instigated by this data gap on a daily basis, shivering in offices set to temperatures set for men or struggling to reach to the top of a shelf – also generally set to the height of men.

Examples of Trying to Solve the Data Gap Problem

One of the main examples given in Criado Perez’s book is the snowplough example in Karslkoga, Sweden. What authorities there tried is clearing up the sidewalks and pathways used mostly by women and children more than the normal roads. When this was done, it saved a considerable amount of money  by preventing more accidents and injuries than normal. Similarly, in London, a data study showed that women use cheaper means of public transport (like buses) in the daytime but switch to subways at night. Nobody knew why this switch happened. But if the bus transport authorities made arrangements to attract women to using the bus more at night (by lighting the areas where bus stops are, for example), they could increase profits.

How to Bridge This Gap?

This is not a deliberate attempt to sideline the female population and put them in a position of danger; it’s the result of years of cultural and gender biases. It would take a good deal of correction to narrow this gap, which people are now becoming aware of. Protests and campaigns are driving companies and government authorities to sit up and take notice. This is indeed a good sign and should catapult a new generation of designs and arrangements that have been made including women in the data.

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