The Gender Data Gap: What is it? & Why Does It Matter? | by Vanessa Mendao

"One of the most important things to say about the Gender Data Gap is that it is not generally malicious, or even deliberate. Quite the opposite. It is simply the product of a way of thinking that has been around for millennia." Caroline Criado Pérez (Invisible Women: Exploring Data Bias in a World Designed for Men)

The Gender Data Gap is a serious issue that affects the lives of women and girls all around the world. However, gender is often overlooked in the collection, analysis and dissemination of data. In this article, we will explore the Gender Data Gap and its impact on women, the importance and challenges of collecting sex-disaggregated data (data that is broken down by gender) and some solutions to address this issue.

What is the Gender Data Gap?

The Gender Data Gap is the lack of sex-disaggregated data. In other words, the Gender Data Gap refers to the systematic underrepresentation or misrepresentation of women and girls in datasets, particularly in areas such as economics, politics, health, and education. This gap occurs because data is collected and analyzed in ways that do not fully consider or account for the experiences and contributions of women and girls. 
In a time when data is crucial for decision-making and policy formulation, the fact that half of the population is consistently left out means that the decisions are made based on inaccurate, incomplete, and biased data. And without accurate data, it is impossible to measure progress and evaluate the impact of any intervention.

Consequences and Impact of the Gender Data Gap

The Gender Data Gap can have significant consequences: it leads to inaccurate and incomplete understandings of the world and reinforces gender inequalities. For example, if economic data only considers the formal labour market and fails to account for unpaid care work largely done by women, it can lead to policies that do not address the needs and contributions of women. Caring for children, the elderly, or sick family members is often not recognized or valued in economic data, leading to an underestimation of women's contributions to the economy.
According to Caroline Criado Pérez, best-selling author of Invisible Women: exposing data bias in a world designed for men, "There is no such thing as a woman who doesn’t work. There is only a woman who isn’t paid for her work."

Similarly, if health data only considers male bodies and fails to account for differences in the ways that men and women experience illness, it can lead to misdiagnosis and inadequate treatment for women.

Caroline Criado Pérez goes even further: "We need a revolution in the research and the practice of medicine, and we need it yesterday. We need to train doctors to listen to women, and to recognise that their inability to diagnose a woman may not be because she is lying or being hysterical: the problem may be the Gender Data Gaps in their knowledge.” 

In this video, Diana Zuckerman, president of the National Center for Health Research, explains why we need more inclusivity in clinical trials - for women, people over 65, and people of colour. 

Another example of the Data Gender Gap in health can be found in the use of crash test dummies. Even though women represent half of all drivers and are more prone to injury in like-for-like accidents, the first crash test dummy designed with the body of the average woman was developed only very recently, in the last quarter of 2022. The fact that male-focused testing was putting female drivers at risk didn’t seem to be a priority for car manufacturers.

Women are still excluded or underrepresented in clinical trials and research studies. This can lead to misdiagnosis and inadequate treatment for women, as well as a lack of understanding of the specific health needs and experiences of women.

Social Impact of the Gender Data Gap

Addressing the Gender Data Gap is crucial for achieving gender equality and creating policies that benefit all members of society. Collecting sex-disaggregated data and conducting gender analyses to identify and address gaps and biases is the main step in the right direction.

The Gender Data Gap can also be evident in education data, particularly in relation to girls' access to education. Girls may be excluded from education data, or their experiences and needs may not be adequately considered in education policies and programs. In many countries, there is no data on the number of girls who drop out of school due to pregnancy, child marriage, or other reasons. This lack of data makes it difficult to identify the root causes and design interventions to address them. In addition, there is a lack of data on the number of women who die during childbirth, which makes it difficult to design interventions to reduce maternal mortality rates.

Tableau, an interactive data visualization software company focused on business intelligence, has created some impressive visualisations that map out the hardest places for girls globally to receive an education.

Women are also underrepresented in political leadership positions. From a gender data perspective, women’s roles in society are still very stereotyped and there is an assumption that women are less interested in politics. The 2011 UN General Assembly resolution on women’s political participation states that “Women in every part of the world continue to be largely marginalized from the political sphere, often as a result of discriminatory laws, practices, attitudes and gender stereotypes, low levels of education, lack of access to health care and the disproportionate effect of poverty on women.” 

The Gender Data Gap is, therefore,  both the reason and a consequence of the lack of female representation in politics. When women do not see themselves reflected in political leadership and decision-making, they may feel that politics is not a space for them and this view leads to a self-perpetuating cycle where women are less likely to run for office or engage in political activities.

There must be a conscious effort from women and men to understand and address the inequalities and stereotypes that hinder the development of effective policies.

Unfortunately, technology is no exception and the Gender Data Gap is also present in this sector.

The International Telecommunication Union (ITU) is the United Nations specialized agency for information and communication technologies and in 2020 they addressed the AI Gender Gap. One of the unsurprising outcomes was that “the more diverse a team or organization, the better positioned it is to provide varied perspectives and spot any omissions.” 

In fact, without enough women involved in AI, the industry risks developing technology that only reflects the perspectives of a small subset of society This lack of diversity can limit the creativity and innovation of AI teams and lead to products that are less effective and useful, and to systems that discriminate against women. It’s crucial to bear in mind that women and other underrepresented groups bring different perspectives, experiences, and skills to the table. As AI becomes more prevalent in society, those who are not involved in developing and designing it will likely miss out on the benefits of the technology leading to inequalities and making it harder for women to compete in the workforce.

Challenges of Collecting Sex-Disaggregated Data

There are several challenges in collecting sex-disaggregated data. One of the challenges is the lack of recognition of the importance of collecting sex-disaggregated data. There is often a lack of political will and funding to collect this data and there is also a lack of technical capacity and expertise in collecting and analyzing sex-disaggregated data.
Another challenge is the lack of available and accessible sex-disaggregated data. This makes it difficult to identify gender disparities and design interventions to address them. Furthermore, even when data is disaggregated, it may not be publicly available or accessible to researchers and policymakers.

Solutions to Address the Gender Data Gap

Addressing the Gender Data Gap requires collecting sex-disaggregated data and conducting gender analyses to identify and address gaps and biases. But what can governments and organisations do to move the needle? 

Closing the Data Gender Gap

In 2021 the World Economic Forum predicted that it will take another 136 years to close the global gender gap. It will be the year  2157.

The United Nations launched the Global Development Index (GDI), one of the Human Development reports. The GDI measures gender inequalities and focuses on people, opportunities and choices.

Several countries have also made commitments to address the Gender Data Gap. For example, the Canadian government has launched the Data Gap Challenge, which aims to fund projects that use data to promote gender equality. In 2019, the United Kingdom government launched the Data Standards Authority, which aims to ensure that government data is collected and shared in a way that is inclusive and equitable.

Despite these efforts, there is still a long way to go to close the Gender Data Gap. Historical bias, limited funding and resources, gender biases in data collection and analysis and an overall lack of awareness contribute to the challenge.

It will require ongoing commitment and effort from governments, civil society organizations, and the private sector to ensure that data accurately reflects the experiences and contributions of women and girls. Hopefully, with continued focus and investment, progress can be made toward closing the Gender Data Gap and promoting gender equality.
 

Resources:

• Invisible Women: Data Bias in a World Designed for Men, (book) Caroline Criado Perez 
• Visible Women (podcast)
• Fix the System, Not the Women, (book) Laura Bates 
• We are Tech Women (website)
• How to Close Tech Gender Gap: Lessons from Silicon Valley’s “Brotopia” (article)
• WOMEN KNOW CYBER: 100 Fascinating Females Fighting Cybercrime (ebook)
• Spellcheck for bias from Geena Davis Institute on Gender in Media
• Visualizing the Current State of the Global Gender Gap
• The World Economic Forum - Global Gender Gap Report 2022

This article was written for International Women's Day by Vanessa Mendao, a Data Literacy/Data & Insights for Business Decisions coach at Multiverse.