COVID-19 Gender Matrix

The matrix below list examples of the intersectional impacts of the COVID-19 outbreak and response. To see further examples click on the intersecting domains. The matrix will be updated regularly. If there are examples you think should be included please email


Access to Resources

Labour/Roles Norms/Beliefs Power  Institutions/ Laws
Risk Women are more susceptible to infection than men among second-generation cases in family cluster Factories are closing in the Pearl River Delta leading to higher male unemployment Female student from a low-income family committed suicide when school switched to virtual-learning Unequal gender norms amongst rural and minority ethnic migrants. Notice on Prevention and Control of COVID-19 for Children and Pregnant Women: Action Plan for pregnant women, mothers, and healthcare workers
Illness/ Treatment Pregnant women with COVID-19 showed higher specificity on chest CT scan Female HCWs report insomnia 5 times more than male HCWs in Huoshenshan Hospital Survey of 146,000 women expressed higher negative impacts on relationships and jobs compared to men. Beijing residents of "medium- or high-risk" areas of infection are completely banned from leaving. The National Health Commission releases the epidemic prevention plan which includes a focus on reproductive health services
Health Systems/Services National comittee of Communist Part of China urge government to include reproductive health expenses under national health insurance   Nurses' working hours, being deployed from another city, and level of qualification, are significant factors associated with PTSD  Rising household "burdens" on male partners while female partners (nurse) work in front line Increased reports on women's significant contribution to manage COVID-19 outbreak: as frontline workers and key leaders in mobilising community Free cervical and breast cancer examinations for low income wome, and in-patient service without deposit for low-income groups
Social Impacts Women led civil society groups were essential and effective in mobilising resources during pandemic 70% of civil society membership are female (and rising). High school students organised to help street cleaners with free masks, food and money. Divorce rate is rising and women are initiating proceedings more than men President Xi suggests the Women's Federation should actively participate in epidemic prevention and control
Economic Impacts High numbers of women sought health care insurance after COVID Women's labor participation rate dropped from 73% to 65% during COVID-19 All-China Women's Federation argues that 'return to work' policy discriminates women. Policy promotes 'stay at home' childcare duties on women Female workers in a male dominated industry (road freight) ask that skill competency indicators is based on work experience, not sex difference Reccomendation on return to work prioritised 5 male-dominant industries (e.g. the logistic industry which employs 95.08% male workers)
Security Impacts Beijing food supply reported stable despite the spike in COVID-19 cases 90% of domestic workers (majority female) don't have a formal agreement with their employer, 1/3 need financial assistance, 1/4 need housing Rise in discrimination, social stigma, and sexual harassment towards domestic workers (majority female) Police issued 2,004 Personal Safety Protection Orders (prevention of violence response orders) during COVID-19 Introduction of Mandatory Reporting System on Violence against a Minor (Child) (Trial) 


The COVID-19 gender matrix aims to provide a rapid snapshot of gendered and intersectional impacts of the outbreak and response. The China (mainland) matrix  includes findings from searches conducted between 20 January 20 and 15 June  2020. Searches were conducted using Chinese and English keywords in Baidu, CNKI database, and through targeted searches of government and organisational websites. We reviewed results, selecting those that presented concrete examples of the impacts of COVID-19 (including journal articles, research papers, speeches, press conferences, and statement). Each selected example was then coded based on a horizontal and vertical category in the matrix. Where more than one example was found per category intersection, we selected the most 'suitable' for the front-facing matrix, but we do urge viewers to view other examples in the linked pages. Decisions regarding coding and which examples to list in the front-facing matrix were discussed with the team to achieve consensus and thematic continuity.

We recognize that the matrix is not a complete picture, but a rapid snapshot, and that there is substantial room for bias to shape its presentation. If you see particular gaps or omissions in the matrix please let us know. This is a dynamic process and we would appreciate the feedback. Please email