This activity considers quantitative data that is useful for gender analysis, with a focus on sex-disaggregated data.
- What is sex-disaggregated data and why is it important?
- What are some examples of sex-disaggregated data and its uses?
- What to do if I can’t access this type of data for gender analysis?
Developing effective policy relies on sourcing, understanding and using quantitative and qualitative data. Quantitative data can be collected directly by organizations including survey research and structured observation, or via secondary data sources such as administrative data or education data. There are many different approaches that can be applied when analysing and interpreting this type of data. For gender analysis sex-disaggregated data is very important. It is quantitative data that counts genders separately.
What is sex-disaggregated data and why is it important?
Sex-disaggregated data is quantitative data that counts genders separately. For example, if your economy captures data about the literacy rates, and if the data can be broken down into the literacy rates for men and women, this data is sex-disaggregated.
Sex-disaggregated data is important in gender analysis because it helps you:
- Understand the current barriers, opportunities and access for different gender groups
- See how policies and programmes impact gender groups differently
- Track changes for different gender groups over time
APEC economies are encouraged to collect and use timely, reliable and quality sex-disaggregated data to be able to quantitatively assess differences across genders, and measure progress of gender equality initiatives including women's economic empowerment.
Some economies differentiate between sex and gender-disaggregated data. However, the term sex-disaggregated is still often interchangeable for many data analysts with gender-disaggregated. As such, Empowering Change uses the term sex-disaggregated throughout.
However, using sex-disaggregated data is only the starting point for effective gender analysis. The more data that can be collected and categorised on different population groups, the better and more nuanced insights can be for policy professionals. See Activity 2.4 for understanding and applying best practice intersectional approaches.
What are some examples of sex-disaggregated data and its uses?
They will help you,
- Identify available data
- Identify where you may need sex-disaggregated data for your project
- Identify gaps in the data and prioritize your needs
Below we set out examples of possible sources of sex-disaggregated data. Care must be taken when judging the quality of datasets including sample size, and quality of collection and analysis.
APEC sex-disaggregated dashboard
The APEC dashboard is a good starting point to gain an overview of country-level differences between women and men across APEC economies. The dashboard maps differences against the five pillars of women’s economic empowerment. It highlights data gaps at economy level. For example, there remains incomplete data across APEC economies on the average time women spend doing unpaid work; the percentage of firms with female owners and female top managers; and the numbers of women working in the fields of STEM and research and development (R&D).
International data sources
The United Nations and other international organizations have population level data.
Some economies have sex-disaggregated data available through their own data repository. For example, Canada has a gender, diversity and inclusion hub.
Data for specific projects and programmes
Some economies also collect data for specific programmes and projects. For example, not-for-profits and advocacy groups often collect sex-disaggregated data for their projects, and can be powerful allies in collecting or sourcing data. For example, APEC's Compendium of Best Practice: Women in Agriculture or Fisheries.
What to do if I can’t access this type of data for gender analysis?
There are many challenges to collecting sex-disaggregated data if it is not a mandated activity including cost, time and expertise required. One solution is to develop research surveys or other forms of engagement to find out information for your project. This can be a short survey, but does require survey design expertise and analysis. Documents including A guide to good survey design can support this activity.
Alternatively, you can request to add gender-related question/s to an existing survey that would support your policy project.
Another solution is to use participatory research techniques, engaging directly with people impacted by your policy. See Activities 2.2 Qualitative Data, and 2.5 Participatory Research, for further information.
Chile: using sex-disaggregated data improves women's leadership and opportunities in fishing and aquaculture sectors.
Policy makers in Chile collected and analyzed sex-disaggregated data to identify the gaps, barriers and inequities faced by women working in the fisheries and aquaculture sectors. They found:
- Work done by women carried less value than work done by men
- Few women held leadership roles
- Limited access to information
- Women did not acknowledge themselves or the value of their participation in the productive chain.
Policy makers used these findings to design actions aimed at addressing the challenges faced by women in Chile. These actions include:
- Developing the Gender Map in the Chilean Fishing Sector, which provides geo-referenced information on the participation of women and men in fisheries
- Providing childcare at workshops and exhibitions about strengthening leadership, empowerment, entrepreneurship, and financing options
- Establishing International Meetings for Women of Small-Scale Fisheries, where women gather to discuss their experiences and their needs.
- Providing opportunities for women to promote their businesses
These actions have made women from the fisheries and aquaculture sectors more visible in Chile. In 2020, there were 1,094 women in leadership positions in Chilean artisanal fisher’s organizations.