Without Actionable Data, Gender Equality Will Remain Out of Reach

Jackeline Alba Contreras, outreach manager with Cosas de Mujeres, a WhatsApp-supported platform for women, at an event in November 2020 in Cúcuta, Colombia. (Photo courtesy of Julia Zulver and Ladysmith)

Since Hillary Clinton’s spearheading of what she called a “gender data revolution” in 2014, numerous initiatives have been launched to generate more and better data on the lives of women and girls. We've seen important efforts to strengthen the collection of sex-disaggregated data in national statistical systems, and we've encountered more and more novel datasets that explore gender and its relationship to climate change, civic participation, medical research, the COVID-19 pandemic, among other issues.

These projects take on many forms. Some involve women and girls as data producers. Some focus on new and more sophisticated indices to track and measure gender inequalities within and across countries. Others harness unconventional “big data” generated by digital technology.

The progress is exciting, but simply having more and better gender data doesn’t guarantee it will bring about equality between the sexes. Nor does it ensure the data will be used at all: Organizations involved in increasing the uptake and impact of available data make up the smallest share of stakeholders in a global gender data industry largely focused on data collection and publication, according to Data2x.

To solve these problems, data must be designed in a way that catalyzes action. Only then can it fulfill its radical promise to realize a “gender data revolution.” That means practitioners, researchers, donors, and activists must push for an approach that does more than collect information and make it freely available.

Bridging the gap between gender data and action in the form of feminist policy change has been a preoccupation of ours since researching the hidden, gendered costs of the world’s biggest anti-poverty programs, conditional cash transfers, and the limitations of statistics for addressing the drivers of gender-based violence. It’s also a guiding motive of Ladysmith, a feminist research collective that we co-founded.

Ladysmith has experimented with making gender data actionable through two very different initiatives. Cosas de Mujeres uses WhatsApp to generate data on sexual and gender-based violence (SGBV) in Venezuela and Colombia as crises and conflict fuel mass migration in the region. We launched it in response to claims that SGBV wasn’t being addressed due to a lack of data on the issue—even while the perpetration of SGBV was widely acknowledged. Another project, a collaboration with Facebook’s Project 17 and Data for Good teams, is an experiment in taking the results of the Survey on Gender Equality at Home and making them actionable. The survey—conducted in July 2020 on Facebook's platform in 80 languages with people all over the world—generated nearly half a million gender-disaggregated responses about life at home.

Among the many insights these initiatives gave us into how to make gender data actionable, four stand out. Practitioners, researchers, and donors who want to connect gender data to real social change should:

Identify the Data That Will Solve Real Problems

Deeply understand the needs of the people and organizations you're trying to help with data before collecting any information. Skip this step, and all your hard work may fail to address the real problem, even if it fills a data gap.

When data projects are thoughtfully designed, they can make a big difference. In Brazil, the Cadernetas Agroecológicas (agro-ecological notebooks) project was designed to address a data gap around women’s contributions to family agriculture, a sector worth $55.2 billion annually in the nation. The organizations behind the logbook projects, such as Sempreviva Organização Feminista and those in the Semear International program, identified a data gap driving a real-world problem: The invisibility of female farmers’ labor in official accounts prevented them from accessing the economic resources available to their male counterparts. In the project, women record the details of what they have sold, given away, exchanged, or consumed. By using this data, they have gotten access to government programs and new markets. They have also gained recognition of their contributions in their communities and society more broadly, helping push gender parity forward.

During the initial design phase of Cosas de Mujeres, we surveyed service providers—sexual health nonprofits, social workers, media professionals, and women’s rights groups—in Cúcuta, Colombia, to identify the data they needed to make their work easier or more effective. We learned that some of them needed data disaggregated not only by gender, but also by age, neighborhood, and type of violence. This parsing gave them insights into the experiences of the specific populations they served, such as sex workers or adolescents.

Not all data needs can be met, however, and there will be tradeoffs. In the cases of Cosas de Mujeres and the Survey on Gender Equality at Home, some safety and privacy concerns related to data disaggregation—protecting people's identities, for example—call for a constant reevaluation of how to break information apart and how specific it can be before handoff to a partner.

Help Your Research Participants

To help avoid data collection fatigue among people who are the subjects of repeated research projects that don’t always have a clear impact on their daily lives, try to help them solve the real problems they may be facing due to inattention to or ignorance of information about their plight. When our Cosas de Mujeres team heard from women that authorities were not responding to reports of gender-based violence, we expanded our work from just gathering and analyzing data to explaining and promoting it. For example, with the feminist organization Corporación - Mujer, Denuncia y Muévete!, we created a workshop for frontline police officers. The program leveraged our data on gender-based violence and outlined steps the officers could take to ensure women had dignified experiences when accessing the justice system. These data-driven engagements helped to generate understanding while also strengthening coordination between different stakeholder groups, thus improving local gender-based violence responses.

For the Survey on Gender Equality at Home project, we took on a similarly expansive approach to our work. We didn't just gather and parse the data—we also reached out to people in our network of academics, journalists, and policymakers whom we knew would be interested but may have been unaware that Facebook could be a source of such information. Though this was not likely to solve any of the research participants' short-term problems the way the Cosas de Mujeres workshops did, it would help raise awareness of the issues, possibly generating solutions over a longer period of time.

Provide Tools and Training

If data is too difficult to understand, it may never be used, and few people have the time, training, or resources to deal with complicated datasets. This is especially the case with "big data" generated by digital technology.

To ensure your research is accessible, consider creating simple visualizations that can be downloaded by anyone for use in news articles, academic papers, PowerPoint presentations, or other forms of content. Ladysmith worked with the data analytics company Azavea to do just that, creating a portal to present and distribute the results of the Survey on Gender Equality at Home.

Once you've got people engaging with your data, you need to make sure they use it right. If they haven't participated in generating a certain dataset, they may misunderstand what it can and should be used for. They could end up making poor policy choices, misappropriating funding, or even doing harm based on their misinterpretations.

In conversations with journalists and social sector organizations about the Survey on Gender Equality at Home, we noticed they wanted to make cross-country comparisons; they wondered, for example, if women in the survey were more concerned with food security in Canada or Mexico. The data, however, was better suited to make comparisons within countries. To clear up the confusion, we offered tutorials and provided examples of questions that could and could not be answered with the data. UN Women and UNDP used the same tactic with the launch of the COVID-19 Global Gender Response Tracker.

Follow Up

Ask your users what was and wasn’t useful about your data and what they might need next. This can help improve datasets in the future and increase their longevity, which in turn increases their value because more people have more time to become familiar with them.

Before the second release of the Survey on Gender Equality at Home, Project 17 held multiple feedback sessions with users of the data. These led to new questions—about gender relations and care work across households, for example—that went into the newest version of the survey. The feedback also led to the reformulation of some existing questions where the data generated was difficult to interpret in a precise manner. In other cases, the changes enabled the generated data to be used in analyses with other, complementary datasets. That, in turn, allowed more nuanced insights that amplified the explanatory power of singular datasets.

Sometimes, follow-up may be less about the data and more about the problems it has highlighted. For example, given that Cosas de Mujeres data also revealed the havoc the pandemic wreaked on people’s livelihoods in Colombia, we told funders that responses to gender-based violence need to be integrated with social protection programs; those can include cash transfers and opportunities for formal employment, both of which provide women with economic resources that can shore up their ability to leave a violent situation.

The Focus for the Future

As funders, academics, and practitioners working with limited resources on achieving gender equality, it can be far too easy to get distracted by quantifying the world's many woes rather than making sure data solves the real and costly problems of the people behind the numbers. Yes, many gaps in gender data remain. We know we must fill them to have a chance to make effective, evidence-based policies. But that is just the first step. Unless we ensure our data connects to social action, justice may remain out of reach, no matter how thoroughly we tally the inequities around the world.

Read more stories by Tara Patricia Cookson & Lorena Fuentes.