Gender-Neutral Approaches will Exacerbate InequityExpected Impact 4

years and above from 1972 to 2019, as explained by an increased longevity and lower fertility rates. The low level of fertility rate has led to a smaller portion of young people as a percentage of the total population. In 2018, women and men were expected to live to the age of 78 and 71 respectively. This age range was estimated to be 61 for men and 66 for women in the seventies. In addition to the increased longevity, there is also a progressive ageing within the elderly population itself. Taking into consideration the specific interests of the female elderly population would ensure that they are not left behind in decisions that impact on their lives.

With women’s needs and interests not being homogenous in nature, an intersectional approach to addressing women’s practical and strategic needs should be at the center of analysis at the decision-making table.

Expected Impact

Lack of diversity in perspectives and policies that are not gender sensitive in the absence of a limited number of women at the decision-making table Mitigation Strategies

Inclusion of voices of women at the community levels to ensure that their practical and strategic needs are accounted for in decision making,

Taking into account women’s intersectionalities in policy formulation and implementation, including interests of the elderly population

Gender mainstreaming in COVID-19 response and preparedness plans

EXPECTED IMPACT 4:

Gender-Neutral Approaches will Exacerbate Inequity

UN Women reports that only 30.7% of indicators needed to monitor the SDGs from a gender perspective are available in Mauritius 33 . Moreover, at present, it is noted that there is limited sex-disaggregated data in different initiatives being undertaken to address COVID-19, for example, the number of beneficiaries of the SRM disaggregated by sex, age, location would be essential in decision

33 UNWomen 2020, Country Data: Mauritius, Available Online at https://data.unwomen.org/country/mauritius, Last Accessed 18 April 2020.

making. Data on gender-based violence is also limited and official statistics on reported cases of domestic violence are stored separately at different levels.

It is critical to be guided by sex-disaggregated data to formulate policies/ programme/ projects to ensure, for example, that any gender gaps are being redressed; and that any initiative undertaken is guided by an evidencebased analysis.

Formulation of gender sensitive performance indicators remains equally important to inter alia, monitor successes of policies/programmes; ensure that targeted actions are reaching intended beneficiaries; and stock taking to inform new agenda setting.

The collection of gender disaggregated, in addition to sex disaggregated data remains crucial in order to examine more in-depth the gendered impacts of COVID-19. Gender specific issues/topics/questions should be considered for inclusion into relevant impact assessment Studies for an analysis of specific gender issues for subgroups based on gendered identities.

Post lockdown, an open data system populated from Official sources that thave continued to collect data during confinement, as well as informed from a civil society organisation (for example Shelters, NGOs) perspective would provide a broader perspective on how COVID-19 has impacted on women across different domains. Data collection should be seen as an iterative process. Women’s narratives and lived experiences, gathered through interviews, would also be a value added to quantitative data.

Expected Impact

Inequity will be exacerbated with gender neutral approaches Mitigation Strategies

Ensuring that socio-economic impact assessments are gender sensitive to ensure evidence-based policy making

Collecting both sex-disaggregated and gender disaggregated data from different sources, including NGOs and Shelters for a holistic perspective

Formulating gender sensitive gender sensitive indicators for M&E purposes.

Collecting qualititative qualitative data such as women’s lived experiences to complement quantitative data