Gender Equity

Introduction

While writing this update to the State of Open Data chapter on Gender Equity, gender equity seems to be even more of an impossible goal: the #metoo movement has lost momentum; the COVID-19 pandemic has made women, especially from the Global South, bear even more of the burden of care and unpaid work than men;¹ and the United States Supreme Court has reversed Roe vs. Wade, sending a message to the world that women cannot have control over their own bodies. Data alone, specifically open data, cannot change cultural norms that are embedded after years of oppression. However, creating and analyzing open data on gender in its broadest sense, if done right, has the potential to bring new perspectives to create a more equitable society for both women and men, as well as all genders. 

Two books have been published in the almost five years since the development of the Gender Equity chapter in the original State of Open Data publication² that have changed perspectives on open gender data and encouraged conversations on the topic. In Invisible Women, published in 2019, Caroline Criado Perez discusses how data about women and females has been missing not only from our culture, but also in science and design. This has resulted in everyday solutions that are designed to be used by the general population, like seat belts or medicines, actually being designed for men, creating an unequal world. The second book, Data Feminism by Catherine D'Ignazio and Lauren Klein (published in 2020 before the pandemic), takes a deeper look at the data pipeline through an intersectional and power dynamic lens. Both books have moved the gender data discussion from technical to political, asking questions such as: Who decides what is counted and how? Who creates the narratives around data? And, most importantly, how do we transform data from a tool of oppression into a tool of liberation? 

This chapter update explores the move from binary to intersectionality - the main development in open data and gender in recent years. It uses a power dynamics lens to analyze this change, building on points made in the two books mentioned above. Since the data pipeline is complicated, this update will look at three main components to help focus the discussion: data publication, data use, and decision-making. 

Component 1: Data Publication

Data publication was one of the first components of the data pipeline to be focused on in the field of gender and open data. Nevertheless, it still faces the same old challenges. The Open Data Inventory³ produced by Open Data Watch shows that countries are publishing more gendered data, but at a slow pace. Non-gendered statistical data themes like poverty and income, or the built environment, are improving in publication faster than gendered categories, such as food security and nutrition or crime and justice. In areas that are inherently affected by power dynamics and are often based on oppressive power, like crime and justice, data is - not surprisingly - the least open and progress is slow.

During the COVID-19 pandemic, we saw first-hand how governments collected gender data not only as a means for monitoring, but also as a way to plan for recovery. However, the Open Data Inventory states that only 53% of countries published sex disaggregated data for numbers on cases and deaths, while 22% of countries published disaggregated data for deaths only, and 35% didn’t publish any sex-disaggregated data at all. 

It is also important to note that published data still mostly looks at gender through a strict binary lens (male and female), excluding other gender options. This means that data often ignores non-binary people or transgender people. Generally, collecting gender data means having difficult conversations with culture and power barriers that don’t allow us to think beyond the binary. Moreover, minority groups are rarely consulted on the topic, and while collecting data on these aspects can be crucial in some contexts, it can be dangerous in others. 

You Need More than Gender Data to Make Some People Visible

In 2023, the view on gender (and self-identification) and women’s rights is not seen as a basic human right but as a ‘culture war’. As part of this war, civil society groups and governments are challenging calls to collect sex and gender data. In the United Kingdom, for example, the sex question in the 2021 census was due to change to include more than two options. But a lawsuit brought by a civil society organization blocked the change, and as a result, the question format will stay the same as it has been since 1801. In Australia, the Australian Statistics Bureau asked whether participants identified as “male”, “female”, or “non-binary” in the country’s 2021 census but left out questions about sexual orientation, gender identity, or variations in sex characteristics. These two examples illustrate how, while our scientific knowledge and our cultural norms about sex have changed, the way we collect data has not due to legal systems still rooted in patriarchal norms. In the Global South, Kenya has been one of the first countries to introduce a new gender category - intersex - a move that will allow us to understand better the challenges intersex people face. 

Gender and sex questions are not only important to understand individual needs, but can help expose deep injustices as well. Trans women suffer from gender-based violence, but are often missing from statistics.⁴ Trans people are known to be more likely to die than cis-gender people,⁵ and having little to no data about them makes it hard to devise policies to help them or to promote their rights. The visualizing transgender day of remembrance project aims to emphasize visibility and awareness around anti-trans violence and to cultivate a sense of solidarity and legitimacy around transgender identities. This project tries not only to look at hard data points but to relate stories and memories to convey pain and gratitude and to guide action. 

Visualizing Transgender Memorial Day 

Meanwhile, we still lack intersectional data across the board. For example, how much data is disaggregated by race? By education level or ability? By rural area and city? In Southeast Asia, for example, there is almost no data on gender-based violence and femicides, especially not from minority groups. However, there is some data on human trafficking and migrant workers. Intersectionality in this data matters, since it surfaces the fact that most trafficked women are likely Indigenous or members of ethnic minorities, showing the complexity of the topic and how myths about it differ from the reality.⁶ If data on femicides were intersectional, it could lead to better policies to protect these women, but currently, they are invisible.

New Initiatives in Data Publication

Steps are being taken to improve data publication when it comes to gender and sex data. In 2022, Equal Measures 2030 - a collaboration of national, regional, and global leaders from feminist networks, civil society, international development, and the private sector that aims to strengthen data-driven advocacy for girls’ and women’s rights - took the step to list data gaps in gender data, explaining why they are important and how we might close them. The Gender Data Lab takes a different approach for data collection, creating a Comprehensive Knowledge Archive Network portal where communities can upload and share their gender datasets. The Gender Data Lab also collaborates with organizations to help in the collection of gender data, providing toolkits and volunteer assistance in the data collection process. 

Component 2: Data Use

Where we have data, even if it is not perfect, we can use it to create pieces of advocacy to support equity for women and girls. The 2022 SDG Gender Index uses cross-cutting data to show the ‘big picture’ progress on overall gender disparities and in the individual Sustainable Development Goals (e.g., nutrition, health, education, and work). Using a range of different sources, some collected by multilateral bodies, such as UN agencies, the index shows that little progress has been made in the past five years to achieve gender equality. 

The use of gender data also remains unsatisfactory. While partially this can be attributed to the lack of data and bad data quality, some of it is due to a lack of skills and knowledge. For example, 52% of women around the world are not connected to the internet.⁷ It is hard to teach technical data skills via telephone or with low bandwidth (try to work on Google Sheets on your phone and you will see how impossible it is to use). When women and non-binary people are online, they also tend to be targets of cyberbullying, making them less likely to participate. The Web Foundation has been encouraging companies to be transparent about gender-based violence online and to create databases to help find solutions to this problem. When women and girls do access the internet they often still remain absent from data science and artificial intelligence (AI) discussions in both the Global North and South.⁸ Research initiatives like Women in Data Science and AI try to help guide policy decisions on the topic, but there is still much work to do. 

Grassroots organizations that work on feminist causes do not have the capacity, either in terms of funding or skills, to use data in their work.⁹ However, there are some initiatives aimed at addressing this challenge. For instance, the Gender Data Fellows initiative of the Tableau Foundation and Equal Measures 2030 identified 16 data journalists in Kenya and India and trained them on gender data, focusing on key media allies for grassroots organizations. They then created data stories based on open data and opened data themselves. Meanwhile, Pollicy - a feminist collective of technologists, data scientists, creatives, and academics working at the intersection of data, design, and technology - created VOTE:Women, a programme whereby women politicians learn data skills that could help them be elected to positions of power. 

We need to do more. We need to discuss with more regional and local feminist networks and offer them support with data. We need to think through, and incorporate, intersectional gender perspectives within whatever project we are working on. We can give women and girls power by giving them the language, tools, and means to use data, as well as the power to tell their own stories and narratives backed by data. 

Component 3: Decision-making

In this section, I do not want to discuss data-influenced decision-making, but rather who gets to choose what data is collected and used. This includes topics such as creating data infrastructure, crafting new technologies, and creating policies about data. In short: the governance of data in creating a data reality. 

Women do exist in the data leadership space, but they don’t dominate. There is no comprehensive research to establish how and from where open data is being created and who governs it. The general sense is that while their numbers are increasing slowly, women in the data community, especially from the Global South, are still not very visible. Community research conducted by Open Heroines revealed that the majority of activity is in the Global North, mostly the United Kingdom and the United States.¹⁰ 

If people are missing from the creation of data infrastructure tools, the data will tend to be more biased toward men and reinforce existing power dynamics. However, getting women to the table is hard. Not many women have access to study how standards or data governance frameworks work. In most cases, the women missing from these discussions are Indigenous, ethnic minorities, and rural women. However, we cannot assume that these groups do not understand how data works. We need to actively look for them or just listen to them when they are seeking a seat at the table or for help to participate.

Conclusion 

This author would like to say that there was a lot of progress in the field of gender equity in the last four to five years, but the opposite is true. Political turmoil, a global pandemic, and the lack of funding has slowed the progress on publishing, using, and leading open data for gender equity. To advance further, we need to take the following steps: 

• Think about gender in every open data project. Some projects need to focus exclusively on gender equity, but when working on any current and new open data projects, we need to think how gender intersects with them and ask the questions: Can open data help shift power dynamics? Who should we consult before publishing data? One good example of this approach is the work done by the Open Contracting Partnership, which added a gender focus to its approach in recent years. This made its work more relevant to women and minority groups and helped them access more opportunities for public procurement.

• Start conversations about both sex AND gender data, while considering intersectional data too. We need to start discussing both sex and gender in order to really understand gender equity. See how the Femicide Data Standard discusses this in their work. 

• We need to make a commitment to better understand who makes decisions in open data: Who they are, what their gender is, and where they are from. Only by understanding who is making the decisions can we understand who is missing from the discussion and how to include more diverse people to lead on open data.