Gender data gap

Gender data gap (abbreviated GDG) refers to missing or underrepresented data collections for a specific gender in data collection procedures that are relevant to social, economic, medical, cultural, or security aspects. In most cases—though not always—the gender data gap puts women at a disadvantage. In addition, the term also refers to the lack of survey data that only concern one gender but would have economic and political consequences, such as the amount of unpaid work in women's household activities or in raising children and caring for relatives. The term is one of a number of gender gaps that have been identified in the context of gender studies over the past 20 years and point to the institutionally disadvantaged situation of women in society.

Terminology

When examining statistical data on the population and the stage of development of member states, it became clear within the UN that important data on the situation of women, such as educational level, domestic violence, and income, was not being statistically recorded. With the aim of educating institutions and countries about the relevance of such data collection, the Inter-Agency and Expert Group on Gender Statistics was founded in 2006. Its mandate is to “review and identify key initiatives and programmes that support and enhance national statistical offices' capacity to develop gender statistics” so that gender data can be collected more systematically. The aim is to close the gender data gap in national statistics. Since then, more and more statistical institutions have been addressing the issue and attempting to close the gap.

The term became widely known in 2019 with the publication of Caroline Criado-Perez's book Invisible Women: Exposing Data Bias in a World Designed for Men. Through a series of interviews, reviews, and reports, the topic reached a wider audience and sparked academic and feminist discussion on the gender data gap in the media. With her extensive research, author Criado-Perez shows how women are not only disadvantaged in the labor market, but also negatively affected by the availability of data, which is mostly based on benchmarks for men. The focus on the gender data gap reveals methodological problems in studies and thresholds in science and technology, and is also relevant because of its social impact, especially on women.

Examples

Algorithms

The gender data gap is also apparent in modern algorithms and self-learning AI systems. These algorithms learn their functionality from training data, so if certain information in this training data is missing, distorted, or underrepresented, it cannot be incorporated into the algorithm, or can only be incorporated with reduced accuracy. The technologies and their training data reflect society in terms of values and knowledge. For example, when Amazon introduced an AI system for selecting applicants, it learned the bias in favor of men that existed in past human hiring decisions and thus also favored men in the selection process. Such algorithms can also reinforce prejudices: an AI system trained to recognize genders in images often misclassified men in the kitchen as female because the training data showed more women in kitchens than men, and the algorithm had learned this correlation. There are also many so-called “health trackers” that misdiagnose diseases in women more frequently than in men due to underrepresentation in the data. The consequences of this bias can also be financially significant. Cases have been reported where women’s creditworthiness and credit card limits were set to be lower than men’s in comparable situations due to algorithms.

Medicine

Gender medicine, or gender-sensitive medicine, deals with the gender data gap in medically relevant data collection procedures. Missing or low percentages of female participants in medical studies lead to one-sided research results. This can influence diagnostics and lead to misjudgments in medication administration and dosage. Vera Regitz-Zagrosek, professor of gender medicine and former director of the Institute for Gender Research in Medicine at the Charité university hospital in Berlin, explains in her book Gendermedizin. Warum Frauen eine andere Medizin brauchen (in english, Gender Medicine: Why Women Need Different Medicine) that such a data gap is already noticeable in the knowledge imparted during medical studies about “hormones or the normal values for blood values and oxygen carriers in the blood.” A prominent example of the data gap is that, in the context of the COVID-19 pandemic, more severe disease progression was statistically detectable in men, while women experienced more severe vaccine reactions, raising questions about possible gender-sensitive adjustments of vaccine dosage.

Sources

1. Gender Data Gap: Wie Frauen in den Daten vergessen werden. In: humanrights.ch. 29. Juni 2021, abgerufen am 17. März 2023.
2. Inter-Agency and Expert Group in Gender Statistics. (PDF) 2013, abgerufen am 29. Juli 2020.
3. GrrlScientist: Invisible Women: Exposing Data Bias In A World Designed For Men. Abgerufen am 24. August 2020 (englisch).
4. Süddeutsche Zeitung: Rezension – Caroline Criado-Perez: „Unsichtbare Frauen“. Abgerufen am 24. August 2020.
5. Bridging the AI gender gap: Why we need better data for an equal world. In: itu.int. International Telecommunication Union, 25. September 2020, abgerufen am 16. März 2023 (englisch).
6. Caroline Criado-Perez: We Need to Close the Gender Data Gap By Including Women in Our Algorithms. Abgerufen am 22. Juni 2020.
7. science ORF at/Agenturen red: Die Welt ist auf Männer zugeschnitten. In: science.orf.at. 9. Januar 2023, abgerufen am 16. März 2023.
8. Mariana Friedrich: Gender Data Gap in der Medizin – Wenn ein Datenleck lebensbedrohlich wird. In: Frauen aufs Podium. 1. Februar 2022, abgerufen am 16. März 2023 (deutsch).
9. Katja Bigalke: Gender Data Gap in der Medizin – Männer als Standard. In: deutschlandfunkkultur.de. 22. August 2020, abgerufen am 16. März 2023.
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