What has gender got to do with Chemicals?

Gender refers to sociocultural norms, identities, and relations that: 1) structure societies and organizations; and 2) shape behaviors, products, technologies, environments, and knowledge (Schiebinger, 1999). Gender attitudes and behaviors are complex and change across time and place. Importantly, gender is multidimensional (Hyde et al., 2018; Dubois et al., 2025) and intersects with other social categories, such as sex, age, socioeconomic status, sexual orientation and ethnicity (see intersectionality. Gender is distinct from sex (Fausto-Sterling, 2012). Historically, gender definitions have been based on a binary concept – women and men. In most key UN definitions, this binary framework continues to dominate, including in chemical policy. To avoid exclusions, newer definitions and guidelines often additionally use the term “all genders”. Beyond defining gender, applying a binary concept within the difference approach to Gender Mainstreaming remains valuable, productive and necessary, as it allows for comparing the experiences of women and men. Analyzing these differences and using binary-disaggregated data provides insights into social structures and inequalities. While we often work with binary categories for analytical purposes (also in chemicals management), we want to do so with a clear awareness of their limitations and with the aim of building more inclusive approaches in the future.

Gender-disaggregated data is often confused with data disaggregated by sex. But biological sex disregards the socially built power relations and gender norms that exist between and among males, females, and people with other gender identities, ignoring the cultural and systematic disparities that exist. Gendered power relations have significant influence on data collection efforts, health-seeking behaviors, vulnerability to risk factors for non-communicable and other diseases, and the impact of health policies. To understand the root causes of gender inequalities, gender-disaggregated data asks the “who questions:” Who does which tasks? Or who has a higher prevalence of a particular disease? Gender-disaggregated data is important to better identify exposure routes.

Sex-disaggregated data is typically collected and analyzed in two biological categories: males and females, and then used to develop programs and policies. Sex-disaggregated health data tells you the numbers of men and women affected by a particular disease, condition, or cause of death (Gender Equity Unit, 2024). For example, human biomonitoring data is often disaggregated by sex for comparing population exposures between the sexes. When data is not disaggregated by sex, it is more difficult to identify real and potential inequalities (UNICEF, 2017).