FEMM

Committee on Women’s Rights and Gender Equality

The story of Karlskoga

In 2011 a small town in Sweden, Karlskoga decided to research if snow-clearing could be sexist. It started as a joke at first, but it quickly became clear that this joke had some truth to it. At that time Karlskoga, like many Swedish towns, began snow-clearing with major traffic arteries, and ended with pedestrian walkways and bicycle paths – a seemingly obvious choice. What they did not realise was that this actually affected men and women differently because men and women travel differently. The data shows that women are more likely to use public transport or walk than men, who tend to use cars more heavily. This means that women are more likely to be affected by snow on pedestrian walkways. This was also reflected in hospital admissions, which during the winter was made up of 69% of women slipping and seriously hurting themselves on icy pathways. Thus, when Karlskoga decided to switch the snow-clearing order, and start with pedestrian pathways instead of streets for cars, they noticed a significant drop in hospital admissions for injuries.

Introduction

From cars that are 71% less safe for women than men, as they were designed using a 50th-percentile male dummy, to 50% of women being misdiagnosed, the world seems to have forgotten women and have excluded them from data collection processes, creating the gender data gap. This gap can be defined as a product of thinking that has existed for millennia. The default “one size fits all” experience of gender has left gaps within our understanding of gendered experiences. It is important to note that this is not done with intention, as this process is deeply intertwined within our cultural and biological history. 

The data gap is also reinforced by incomplete, flawed or lacking data.

The consequences of this lack of data can be dire. The gender-data gap has led to less impactful social and economic policies, and impacts everyday aspects of women’s lives. This can be seen in examples such as transportation or urban planning. Furthermore, due to the data gap, inequalities in gendered social and economic spaces, like the workplace, are often ignored or forgotten. 

The lack of gendered data becomes an even more pressing issue when we consider the ever-growing digitalisation of our society. It already is common practice that Artificial Intelligence (AI) helps doctors with diagnoses, scans through CVS, or conducts interviews with potential job applicants. These AI’s however, have been trained on the existing data sets therefore may be perpetuating the data bias. Nonetheless, closing this gap is not just as simple as the production of sex-disaggregation data in all sectors. It also needs to be written into the legislative framework in order to impact the obstacles women and girls are currently facing due to the lack of data. 

Keywords

• Gender: the characteristics of women, men, girls and boys that are socially constructed. Including norms, behaviours and roles associated with being a woman, man, girl or boy.
• Gender Data Gap: the circumstance that the majority of data collected on which our organisational decisions are based upon, appear to be biased in favour of males.
• Sex-disaggregation: data on males and females that is collected and analysed separately Data bias: an error that occurs when certain elements of a dataset are disproportionately emphasised or overrepresented. This leads to skewed outcomes, systematic prejudice and low accuracy.
• Gender Mainstreaming: The action of implementing a perspective of gender equality at all levels of policies, initiatives, programmes and projects.

Key Actors and Stakeholders

• The European Commission: This stakeholder is the European Union’s (EU) political executive arm and has legislative power. It has a section on fundamental rights that has implemented the strategies to combat gender inequalities.
• The World Bank: Is a global partnership with five central institutions that work towards sustainable solutions that reduce poverty and build shared prosperity specifically in developing countries. They have recently implemented the Strengthening Gender Statistics (SGS) project (see next section). 
• The European Institute for Gender Equality (EIGE): This is an autonomous body of the EU which works on strengthening gender equality in all EU policies and the resulting national policies of Member States. Working with all policies EIGE’s primary focus is to research, collect data and make statistics regarding gender equality in various sectors. 
• The International Telecommunication Union (ITU): This is a specialised agency of the United Nations responsible for many affairs related to information and communication technologies. Therefore, this stakeholder also has dedicated a section to bridging the Artififcial Intelligence data gap. 
• World Health Organisation (WHO): This stakeholder is another specialized agency of the United Nations responsible for international public health. It too has a section dedicated to addressing the gender data gap, now specifically in the health sector
• Member States: These are the 27 countries that share both privileges and obligations imposed by the European Union. These are stakeholders within this topic, as they are impacted by any legislation, policies or political direction the EU wants to take. An example of a political direction is the Istanbul convention for example, which is a treaty that combats violence against women. The Member States may choose to not ratify these kinds of treaties but are encouraged to do so by the European Council. 

What has already been done? 

In 2020 the European Commission introduced the European Union Gender Equality Strategy which aims to present policy objectives and actions that will ensure significant progress by 2025 towards a gender-equal Europe. The main objectives of the strategy is to end gender-based violence; challenging gender stereotypes; closing gender gaps within the labour market and care; achieving equal participation across different sectors; addressing the gender pay gap and achieving gender balance in decision-making and in politics. This strategy is a strong start towards more equality within the EU but is missing explicitly creating a pathway to combat the gender data gap. 

The Strengthening gender statistics (SGS) project is an initiative driven by the world bank for a global engagement on gender statistics, with the focus mainly being within the economic domain. It specifically draws on expertise from the gender group, and the development data group’s Living Standards Measurement Study (LSMS) program, which provides technical assistance when improving gender data production and dissemination of sex-dissaggregated statistics. The project is mainly focused on IDA countries* and is currently supporting twelve of these with a statistical operation with results expected by 2023. Therefore, this project is currently not working directly within the borders of the EU, but is building a framework for gender specific data collection. 

Lastly, the UN women count strategy is an initiative created by the United Nations that want to support the Sustainable Development Goals (SDGs) which aim to achieve gender equality by 2030. However, to do this, more sex-dissagrated data needs to be collected, in order to monitor the implementation of the SDGs. Therefore this strategy seeks to create a radical shift in how gendered data is used, created and promoted. 

What are the key challenges? 

Throughout history, women have systemically been ignored when it comes to being represented within data. This has led to gaps in knowledge within various sectors ranging from government policy and medical research to technology and media. Currently 80% of indicators for gender equality of the SDGs have data gaps, which create problems and obstacles for the women. In addition to this, women only make up 22% of the representation in health trials globally. This means that medicines are exponentially less effective for women. Further obstacles for women can be found in urban planning infrastructures, making it harder for women to commute. The data gap affects the everyday life of women, for example the size of smartphones, which is often times too big for a woman’s hand. 

Furthermore, with the growing use of Artificial Intelligence in many sectors across Europe, such as in health care, the gender data gap becomes even more prevalent and important. The data used within these AI systems is based on pre-existing data, and therefore, is ineffective in improving the quality of life for all citizens 

All of these gaps are reinforced by lack of sex-diseggrated data, alongside lack of representation in research , policy making and media. Women only make up 37% in the media sector, which is not only a consequence of the gender data gap, but continues to reinforce it.

What now? 

Even with all of these factors, there is a lack of a systematic structure that combats these gaps in knowledge. This is mainly due to the fact that the problem has been such a long standing issue. Therefore it is important to both look for a future thinking solution, but also take into account already existing biases

• How can we ensure more sex-disaggregated data is collected?
• What measures towards gender specific data can be set at a local level to build a base for wider international research
• How can we improve previously collected data in order to ensure that there is no gap in knowledge?
• How can we reverse the data bias already existing within our societies today?

Further Research

• This Article written by Caroline Criado Perez who also wrote “Invisible women: exposing data bias in a world designed for men”
• A short video explaining the gender data gap
• A longer video with deeper information on the data gap
• The world gender data bank that illustrates the aspects in which more sex-disaggrated data needs to be collected

Introductory Clauses to the resolution

1. Stressing that the data gap is creating gender-based obstacles in various sectors such as health, technology and industry, which can lead to dire circumstances,
2. Recognising that on a European Level, no clear policy has been created to combat the gender data gap,
3. Emphasising that the rise in Artifical Intelligence (AI) Technology will increase data bias, 
4. Noting with regret that only 15% of countries worldwide have legislations in place that order specialised gender-based data,
5. Deeply concerned that currently 80% of the indicators for gender equality of the Sustainable Development Goals (SDGs) have data gaps,
6. Alarmed by the fact that women only make up 22% of representation in health trials globally,
7. Deeply conscious that lack of representation in research, policy and in the media further perpetuates the gender data gap,
8. Bearing in mind that Member States’ governments are free to develop their own data regulations;