Step 1. Collect information and disaggregated data on the target group
Consider using disaggregated data to inform any analytical exercises. This means that statistics on the target group are disaggregated by characteristics such as sex, gender, age, race, ethnicity, disability, socio-economic status, education level, employment in different sectors, entrepreneurship in different sectors, salary levels, and other relevant factors. When collecting this data, remember some key principles.
- It is important that data always be disaggregated by sex as a primary, overall classification. For example, when collecting statistics on ‘young people’ or ‘older people’, make sure that the target group is disaggregated by sex.
- In addition to quantitative data on specific characteristics, analysis needs to take into account qualitative data on people’s lived experiences. Crucially, it must identify how services are used differently by different people, and what resources should be allocated to address these differences.
- Qualitative research is also needed to identify the causes of inequality. Only by understanding these underlying reasons can we ensure that projects meaningfully advance greater equality. For example, public spaces and public transportation are used by different people – older women and men, younger women and men, children, parents and carers, people travelling to work – in different ways depending on the time of day, their income levels, their work and childcare arrangements, etc. Gender-sensitive analysis must take a broad view of what a range of data tells us about people’s everyday lives.
- It is important to use information from a range of sources (e.g. local and sub-national studies or consultations) and combine various data sources (e.g. data from statistical offices, academic works and policy reports) for a comprehensive understanding of on-the-ground realities.
- When data on race or ethnicity, age, disability or sex are not available, this should be identified as a gap. Activities to improve available data could be part of programmes and local projects. Efforts to improve data could be considered in project objectives and reporting.
- Gender-specific data on work-life balance help to better understand how work and care responsibilities are divided between women and men. Data on employment and time use shed light on gendered patterns of paid and unpaid work.
- It is vital to tailor any analysis to the local context, including by analysing local data. This can be done by involving national or local gender experts, consulting civil society organisations – especially women’s organisations – making use of national research, and triangulating information.
Useful data sources include:
- Gender Equality Index, which provides data from all EU Member States on eight domains — work, money, knowledge, time, power, health, violence against women and intersecting inequalities;
- EIGE’s Gender Statistics Database, a comprehensive knowledge centre for gender statistics and information on various aspects of (in)equalities between women and men;
- Eurostat gender statistics which presents statistics in a way that is easy to understand, complete with a statistical glossary and links to further information, the latest data and metadata.
 EIGE (2019), Gender Inequality Index, EIGE, Vilnius. Available at: https://eige.europa.eu/gender-equality-index
 EIGE (2019), Gender Statistics Database, EIGE, Vilnius. Available at: https://eige.europa.eu/gender-statistics/dgs
 Eurostat (2019), Gender Statistics, Eurostat, Brussels. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php/Gender_statistics