Why Gender Data Matters: A brief examination of the gender data gap in transport

The UN Sustainable Development Goals pay specific attention to gender equity and its role in determining greater inclusion of and safety and opportunities for women. Although not explicit in the targets outlined to achieve gender equity, gender-responsive transport and mobility play a significant role in increasing accessibility for women and girls.* One challenge in realizing gender-equity is addressing the gender data gap. In many cases, the data and subsequent evidence that does exist around all aspects of daily life, including mobility, are either not disaggregated for gender, or based on the “default male” experience, as detailed in the book Invisible Women: Exposing data bias in a world designed for men by author Caroline Criado-Perez.

What is the gender data gap?

Acknowledging the gender data gap in transport means acknowledging that for decades transport planning has limited itself to designing transport systems that organise networks with a more quantitative view, focused more of the number of people moving from A to B and their route choices. This fails to take into account that individual mobility choices are impacted their experiences and expectations towards certain transport modes, and individual capabilities and resources (e.g. age, income, ability). Closing the gender data gap in transport means including an emphasis on qualitative dimensions in data collection to gain in-depth insights and understanding of mobility choices and transport system failures.

Why address the gap?

Addressing the gender data gap matters because women and girls experience mobility and moving in public space, differently from men and boys in a number of crucial ways:

1 – Size Matters: The physiological differences between men’s and women’s bodies means elements like height, width, grade, impact women’s experience of public space.

2 – Mobilities of Care and Trip Chaining: Women are still largely responsible for the mobilities of care, as defined by Ines Madriaga-Sanchez as the trips done to perform unpaid care work like transporting children and running errands, that are often accomplished through combining multiple short trips into one longer journey, also known as trip chaining.

3 – Moving with Children: Women often move through their communities with small children, which means making considerations for pushing a stroller, moving slower to accommodate a child’s pace, and the impact of traffic safety on route choice to minimize potential conflicts.

4 – Safety: An IPSOS study conducted in 2021 found the 80% of women globally have experienced some form of harassment in public space. The fear of harassment has a direct impact on the mobility choices of women and, if left unreported, remains unaddressed by transport authorities with the power to make the necessary changes to improve infrastructure and other measures that could facilitate a safer experience for women.

“When it comes to the lives of [women], there is often nothing but silence. … The gender data gap isn’t just about silence. These silences, the gaps, have consequences. They impact women’s lives every day.” (Criado Perez, 2019). It is only by addressing the gender data gap that addressing women’s transport and mobility needs in a more equitable way can begin.

EUMA2021 Mobility of Women; picture: Heinrich-Böll-Stiftung

Reducing the Gender Data Gap

In 2022, Women Mobilize Women facilitated two data research studies specifically on gender-focused data collection in African cities to help improve the quality and availability of gender disaggregated data. The data and stories collected in each study, produced by Where’s My Transport (WIMT) and Groots–highlight the relationship between quantitative and qualitative data in painting a more complete picture of how women experience transport.

Launching in late 2022, each study provides evidence of what is missed when we don’t take the gender data gap into account when evaluating and planning for gender-responsive mobility. This includes having a clear methodology of how data is collected, who participates, including respondents, data collectors, and who is analysing the data, what types of data is collected, making equal space for qualitative, story and experience-based data, and, of course, how that data is analysed.

Conclusion: The future of gender-responsive data

When we collect gender-equitable data, everybody wins. A greater understand women’s travel patterns creates a more inclusive picture of how everyone travels. The travel needs of women influence the needs of children, the elderly, people with disabilities, and people from varying economic means. When a diverse approach is employed that also considers the inclusion of underrepresented groups, particularly people of colour, then the resulting analysis has a greater reflection of the diverse needs, wants, and behaviours of everyone.

However, it is necessary to be strategic to ensure the same binary patterns of historical data collection are not repeated. This requires investment in diverse approaches to gather data, resulting in a more complete representation of both the data and people involved at all levels of the data collection. In 2021, Women Mobilize Women outlined 6 principles for Bridging the Gender Data Gap. They help highlight steps we can all take to ensure a gender-sensitive and inclusive approach is applied to data collection.

Gender data matters because women represent over 50% of the global population. Without understanding women’s needs, it will remain difficult to achieve the UNSDG of global gender equity. To create more inclusive and more feminist transport systems, women need to be reflected in the data and in the solutions that follow.

* For the purpose of this article, the use of women and girls includes non-binary and transgender persons.

More Blog Posts

A Model Methodology for Closing the Gender Data Gap

To better address women’s transport needs, two studies collected data exploring travel habits in African metropolises. Their unique methodologies can provide a model for how to best collect such data and interpret the nuances it reveals.