Til death do we part: Data’s problem with gender

The recent whirlwind of support of open data has many, including the European Commission, governments and analysts applauding a new era of ‘Data as Culture,’ where individual and collective data shadows track, stalk and cloak us throughout our lives.

From the time we are born, we receive countless involuntary yet socially-necessary data assignments — from phone numbers and social security numbers to intelligence ratings, voting habits and political opinions.

The data shadow formed by one category in particular — sex and gender — is arguably one of the trickiest yet most impactful pieces of data collected. Here’s why.

The Power of the Checkbox

Within seconds of popping out of our mother’s womb, a doctor makes a determination of our gender based on the sex we present, checks a box, and from that moment on, we are stalked by ‘M’ or ‘F’ on passports, airplane tickets, university applications, prison entrance forms, mortgage applications, and the list goes on.

What no one explains to us as newborns, as we lay cozy in our mother’s arms, is that, for example, being labeled Female (F) at birth and treated as women means that the following three facts (among many others) will be true:

• At least 1 in 3 women around the world will be beaten, coerced into sex, or otherwise abused in her lifetime  (General Assembly. In-Depth Study on All Forms of Violence against Women: Report of the Secretary General, 2006. A/61/122/Add.1. 6 July 2006)
• Women make up just 17% of parliamentarians (UNICEF, The State of the World’s Children 2007, UNICEF, New York: 2006, p.56)
• Women account for nearly two thirds of the world’s 780 million people who cannot read. (UNESCO Institute for Statistics, “Adult Literacy Rates and Illiterate Population by Region and Gender,” 2006)

Being labeled as Female at birth therefore means more than just a checked box in a moment.

That checked box (F) is entered into a computer and is paired with the more than 3 billion other boxes checked ‘F’. From that databank then, statistics are pulled based on linkages and similarities between women categorized as Female (F). Statements, such as the three listed above, are consequently formed and are used in annual reports, to promote awareness and to, ideally, achieve what we like to call gender equality.

Gender Data Too Two Dimensional?

But gender-based data, with its simplistic categorization of all humans as either M or F is like saying that there are only two colors in the world. It’s like saying that no one can chose which color suits them most, and that the way they are treated in life (along with the data that is gathered as a consequence), will indisputably be based on that one color assigned at birth. Pick your least favorite color. Now imagine you had to smear that color all over your body every day and that everything from your experiences with violence, to education and politics were determined based on that color.

Along these lines, lets return to the three statements above. All refer to women, not females, because they are inherently based on the assumption that being assigned the category of female at birth is automatically coupled with then growing up and being treated as a woman. Alas.

In the majority of cases, this is true, and it is important not to overlook experiences of sexual harassment, assault and lack of access to education and politics that so many (millions if not billions of) females who do identify as women lack.

But what if you were born with male anatomical traits (like a penis and an Adam’s apple) but identified as a woman? Because of assumptions and/or discomfort among health professionals, trans people are either inaccurately counted in data surveillance methods or left out entirely.

The lack of sex and gender options for trans people forces an allegiance to ‘M’ or ‘F’. The University of California San Francisco’s Center of Excellence on Transgender Health puts it bluntly, noting that this option is “too simplistic and binary to accurately and effectively collect critical information to identify emerging trends, allocate resources, improve health care services, and address service gaps among populations of individuals.”

Transgender people represent anywhere from two to five percent of the population in the US (totaling approximately 700,000 in 2011) and about 2.3 percent of the population in the Netherlands (totaling approximately 390,000 in 2012).

In the Netherlands, an additional 830,000 people identify as having incongruent or ambivalent gender identities and about 1 in 12,000 males and 1 in 34,000 females undergo sex-reassignment surgery.

Given these statistics, it’s important to recognize the gaps in data collection that ignoring different gender identities, dismissing them or miscategorizing people can create.

So here’s some food for thought. Look around you. Look at people’s data shadows. Recognize whether they carry an ‘M’ or an ‘F’ and see all the freedom or baggage that either sex and gender assignment carries with it. Think about your own shadow as well. You might surprise yourself with what you discover.