Guidance

Panel discussion on the gender data gap

Published 3 April 2019

We began our Open Government Week celebrations with a panel discussion focused on the gender data gap. ‘Gender data’ is commonly referred to as data disaggregated by sex, such as primary school enrollment rates for girls and boys, as well as data that affects women and girls exclusively or primarily, such as maternal mortality rates.

Currently, we have no data or poor quality data on issues that disproportionately affect women, which undermined the ability to understand the lives of women and girls and the constraints they face. Data collection is often distorted by gender biases and a resulting gap hinders the ability to determine the size and nature of social and economic problems and design appropriate policies^{[footnote 1]}.

We began the session by thinking about what gender data means. In the words of one of the panelists, Data Researcher and Consultant Ana Brandusescu, the gender data gap is about an existing gap in data about women and about data that women could use to empower themselves economically, socially, culturally. Panellists asserted that this is as a result of patriarchal norms in society and institutions and it is crucial to note that it is not simply about the technology or data, but about the fundamental way we think about gender and power dynamics.

Why does the gender data gap matter?

We then asked the panelists about why should we care about the gender data gap:

• No data means no voice, exclusion, no problems to report. As emphasised by Dr Carla Bonina, Lecturer in Entrepreneurship and Innovation at Surrey Business School, this is particularly salient in important public problems like gender violence. In this case, without gender data, voices of survivors of gender violence are simply not being heard.

• Gender data gap links to the issue of access and addressing the digital divide. Men are on average 33.5% more likely to have internet access than women^{[footnote 2]}. Tonu Basu, Thematic Engagement Lead at the Open Government Partnership, and Ana Brandusescu spoke about the access gap that impacts the efficacy of digital policies, for instance in the spheres of civic technology, delivery of services online, or data rights online.

• Some issues impact women differently, sometimes disproportionately; gender data helps us understand that better. Tonu stressed the need to think through not just data informing policy but gender data on policy impacts. For Dr Mary-Ann Stephenson, Director of the Women’s Budget Group, data disagreggated by sex is central to conducting gender budgeting - analysing budgets for their different impact on women and men, in order to avoid policies that will increase inequality and encourage policies that will promote it.

• Gender data gap because reveals systemic societal issues that need to be addressed, such as the gender pay gap, women’s unpaid labour, women’s time poverty, digital inequality and the digital gender divide, said Ana Brandusescu.

What is the state of the gender data gaps?

• Gaps in intersectional data - according to Mary-Ann, although it is fairly straightforward to get any impact data broken down by sex or race respectively, it is rare to find data simultaneously broken down by two or more characteristics. If we want to know, for example, what has happened to black women’s earnings or changes to the proportion of Asian women in paid work, intersectional data is essential.

• Lack of unpaid economy and care data - in the UK we collect data on time use and specifically on things like care responsibilities in the census, yet we don’t always look at how policy will impact unpaid work. For instance, when spending on social care services is cut, women end up taking up the majority of unpaid work (Dr Mary-Ann Stephenson).

• Underreporting - only about ten percent of women who are raped or sexually assaulted report this to the police. The Crime Survey for England and Wales has done a huge amount of work in increasing reporting rates of these under-reported crimes, but such gaps prevail in other areas. Underreporting can lead to problems not being recognised as such, and as a result, not being addressed at all (Dr Mary-Ann Stephenson).

• Gender pay gap is closely related to the gender data gap and wider societal power dynamics. Gillian Unsworth, Head of Gender Pay Gap Reporting, Government Equalities Office, focused on the case study of a gender pay gap, with an overall message that although the gender pay gap is caused by multiple, complex factors, there is a lot that employers can do to promote equality and improve the workplace for everyone. So far, mandatory reporting has prompted over 10,000 board level conversations about how to tackle the gender pay gap and a business case for gender equality is clear – more diverse workforces are more productive. However, we need to go beyond the data. Gillian emphasised that·reporting is only the first step and employers need to use the data to identify what action they should take to tackle the barriers women face in the workplace. To help drive change, GEO offers employers a wide range of resources to help them address the issues in their organisations.

However, since gender data gap is not only about the availability of the data, but rather about how this data is analysed and what exactly is published, we need to go beyond the data itself and focus on analytical abilities and understanding:

• We need to look at the broader context, e.g. a lifetime perspective wherever possible. As Mary-Ann pointed out, data is often presented as a snapshot. For instance, although data can help identify the pay gap for people who work full time and part time, what it doesn’t show is whether part time work at one period of your life impacts your earnings later. This would require a lifetime perspective data comparing the pay of people who have always worked full time with the pay of those who have worked part time in the past.

• A commitment to gender equity must address both, gender bias in data collection and publication, as well as in patterns of exclusion (Ana Brandusescu).

What can we, as a community of data policymakers and practitioners, do to improve the situation?

Most importantly, through our actions, we need to avoid widening the gap that already exists by not further marginalising the already marginalised and empowering the already empowered (Ana Brandusescu).

We can also:

• Ask for data and keep asking for it

• Ask the right questions. As proposed by Tonu, this can be asking the following:

  • How are gender-targeted policies working in practice? Carla provided a great example of the Latin American Alliance for Civic Technology (ALTEC), a $3.5M competitive fund to support the development and promotion of civic technology solutions in Latin America, include an inclusive gender perspective in several aspects of their funding. They look at the gender perspective when deciding who to fund (and therefore, being part of the core strategy of their portfolio of investment),and they work with those awarded to develop gender lens. This means that they work alongside the winners to diagnose the current status of a gender perspective in the project, to then develop a working plan, and disseminate lessons learned.

  • What data can we get on gendered-impacts of a specific policy? For instance, Canada committed to publishing an analysis of gender-based impacts for all budget measures.

  • What types of data are needed to tackle gender-specific challenges?

  • What types of data give us a better sense of use of services?

  • What kind of data shows us the extent of representation/opportunity gaps? For instance, Germany used its OGP action plan to conduct regular monitoring on the status of women and men in leadership positions in private sector bodies and the public service. This will serve as a framework for implementing the national Act on Equal Participation of Women and Men in Leadership Positions in the Private and the Public Sector.

  • How do we understand equality impact? For example, when the government cut tax on income from shares and investments, it stated that there is no gender impact of this policy because it will affect women and men who have income from shares and investments equally. As Mary-Ann argued, this analysis didn’t look at who is more likely to have investments or own shares. Because men are more likely to have shares and investments than women, they will gain more when tax on this income is cut.

• Mainstream gender into open data commitments, for example through the Open Government Partnership National Action Plans.

• Improve the quality of the existing data. Carla provided an example of Standardisation of Data on Femicide project run by ILDA, focused on improving the availability and quality of data on femicides (killing a woman or a girl on account of her gender) in Latin America. The project aims to first, to understand how each country counts femicides and how the data is constructed, what are the variables considered, the methodology employed and, map the levels of access to the data collected in each country. Then, it aims to create a common methodology to enable comparability across countries, and to help count femicides while establishing a standard of what counts as a femicide and what does not.

• Ensure that gender data is used to deliver more effective policies for all and use gender analysis to measure the impact of policies to help identify where and how policies, practices, or actions may differently impact men, women, boys, girls and other gender groups. Gender analyses can be conducted in partnership with local, national, or international organizations; women’s ministries or governmental gender advisors; or independent consultants (Tonu Basu, Ana Bransuescu).

• Involve all genders in the conversation on gender data, as well as policy development and use. Address gender beyond the binary and at the intersection of race, age, income level, etc (Ana Brandusescu).

• Continue dialogue between the government and grassroots women’s groups such as Open Heroines or organisations with gender expertise such as Women’s Budget Group; and assess how communities are represented, and how they are consulted by their representatives.

Collated by Natalia Domagala with inputs from Ana Brandusescu, Dr Carla Bonina, Tonu Basu, Gillian Unsworth, Dr Mary-Ann Stephenson. This is an open discussion led by DCMS and our civil society partners, not an official statement of policy.

1. https://www.data2x.org/what-is-gender-data/ ↩

2. https://theinclusiveinternet.eiu.com/ ↩