More than ever, data determine every aspect of our lives, from medicine and transport to economics and crisis management, especially in light of the COVID-19 pandemic. Data are primarily sourced from (white) men. The lack of data on women is known as the Gender Data Gap. Most of the data on which organizational, policy, or medical decisions are based appear to be biased in favor of men. Even decisions on issues that primarily affect women, such as reproductive health, are often made without considering relevant data on women.

Four examples to illustrate the Gender Data Gap

• Recently, a study on 1.3 million patients in Canada showed that women operated on by a male surgeon are 15% more liable to suffer a bad outcome, and 32% more likely to die, experience complications and be readmitted to hospital than when a woman carries out the surgery; whereas, women surgeons have no different outcomes for their patients, regardless their sex. The Gender Data Gap explains these outcomes as follows: Medical textbooks primarily rely on illustrations of white male bodies, medical products often have more negative side effects for women, medications and their dosages are predominantly based on studies with male subjects, and male scientists are less likely to incorporate gender and sex analyses in their research.

• Severe consequences of the COVID-19 pandemic affect women more than men. This is partly due to underestimating the extent and dimensions of women’s caregiving work; the complexity of caregiving tasks has increased significantly as a result of the pandemic (e.g. through homeschooling). The existing regulations on the reconciliation of work and family life are therefore not adapted to the real needs of families, and especially of women additionally taking on caregiving tasks.

• More than ever, companies are committed to increasing diversity and equal opportunities in the workforce. However, the strategies developed by the companies are often based solely on male data or data that include women but are biased in favor of men. For example, office thermostats are typically set to temperatures that facilitate men’s (but not women’s) cognitive performance, presumably because the policy concerning the optimum regulation of temperature relies on male data. In addition, based on the data gap on women, companies often lack basic awareness and knowledge about the female body and how it affects the way women work, as well as their performance.

• In the future, the steadily advancing development of AI will most likely continue to reproduce and perpetuate the preferential treatment of men and the discrimination against women. The developers of Guild Technology Inc., an online platform for evaluating applicants for technical professions, analyzed web data to find out how much time applicants spent sharing and developing code. However, these web-scraping algorithms do not take into account specific factors assigned to women in the programming industry. For example, women often use male pseudonyms to avoid harassment when navigating through open source platforms. Because of the AI system not taking into account this use of male pseudonyms by women, it automatically underestimates the qualifications of female applicants.

How to close the Gender Data Gap?

Management science must acknowledge and understand the Gender Data Gap in order to take effective measures on an organizational and societal level. From an organizational perspective, it is of utmost importance to start collecting and interpreting the missing data. This will have a significant impact on existing routines and practices that have been based on the only available (male) data for decades.

Addressing the Gender Data Gap is a necessary step that helps provide valuable insights into related data gaps. Data gaps on other underrepresented groups in the workplace and intersectional aspects should likewise be on the agendas of management scholars, policymakers, and organizational leaders.

The underlying problem of the Gender Data Gap is much more fundamental and more far-reaching than “only” missing data on gender. Organizations and institutions must act on the basis of new data to achieve greater equality for women and, thus, greater equality for all those who do not enjoy intersectional privilege. If knowledge does not lead to action, findings about the Gender Data Gap will get lost.

The study

Sonja Sperber, Susanne Täuber, Corinne Post, Cordula Barzantny: Gender Data Gap and its impact on management science — Reflections from a European perspective. European Management Journal. Available online at https://www.sciencedirect.com/science/article/pii/S0263237322001554