COVID-19 Gender Matrix
The COVID-19 Gender Matrix provides a rapid snapshot of the gendered impacts of the outbreak in each country. It aims to document the wide-ranging impacts across multiple domains to illuminate how gender and other inequities impact and are impacted by the response. The next step of the project will analyze if and how COVID-19 policies respond to these impacts.
A gender matrix is an analytical tool used to analyze how people of different genders (men, women, and with non-binary gender identities) experience an event or health challenge. It promotes consideration of how an individual’s experience of the particular issue (horizontal categories) interact with gender-related considerations (vertical categories). Gender analysis matrixes are also used within programs or interventions to explore how gender power relations may affect the ability of an intervention to meet its objective.
We have designed a COVID-19 Gender Matrix to reflect on how experiences and responses to the outbreak are structured not just by risk, illness, and health services, but also social, economic, and security factors. Our gender considerations aim to take a multidimensional perspective on gender – recognizing gender interacts with access to resource, the roles we fill in society, what is expected of us, and power dynamics. It includes evidence of the ways in which gender power relations manifest to create inequities and/or differences in experiences.
The matrix is intersectional. We aim to document how various factors – such as race, ethnicity, and sexuality – interact with gender to structure inequities.
The matrix is dynamic. Considering the fast-moving nature of the COVID-19 outbreak, we have begun with a rapid gender analysis using the matrix domains to guide our analysis of policy, media, and other documents, which we will update monthly. As we conduct primary research, we will add depth of data and analysis to the matrix.
Methodology: Building off of other gender matrixes, the team collaboratively developed and refined the COVID-19 and gender domains, developing a codebook to define each of the domain intersections. News and web content were systematically searched for evidence of gender impacts of the outbreak and response, and each impact was then classified by domain and entered into the matrix.