Improve insights through an intersectional approach

Activity 2.4

Improve insights through an intersectional approach


Understand the key concepts and approaches to intersectional analysis, and how it applies to policy work.

  • What is intersectionality?
  • How do I apply intersectional analysis to policy?

Sex-disaggregated data helps us understand the experiences of people of different gender. However, looking at other identity data that interacts with gender helps make insights more nuanced and targeted. This is intersectional analysis. Using this data in policy planning helps you look more strategically at target populations. But an intersectional approach is also about identifying and responding to the distribution of opportunities, marginalisation, and systemic discrimination, not just demographics.

What is intersectionality?

Intersectionality describes the way different elements of our identity interact with each other. These elements include for example, ethnicity, gender, disability status, age, religion, marital status, education, indigeneity and pregnancy.

Different combinations of these elements comprise a person’s identity, and lead to different lived experiences. This can include multiple and intersecting forms of structural discrimination, access issues and inequitable outcomes.

For example, the Australian Royal Commission into Violence, Abuse, Neglect and Exploitation of people with Disability (2021) found that almost 90 per cent of women with a disability aged 15 years or over have experienced sexual harassment. This is significantly higher than the general female population that would have been identified through use of sex-disaggregated data alone.

How do I apply intersectional analysis to policy?

Many economies practice intersectionality in gender analysis already, whereas others may be developing their knowledge and skills.

The self-reflective framework in Activity 1.1 will help guide your own awareness of your skills and knowledge about intersectionality.

However, if you are developing these skills it is important to consider,

Quantitative approaches

To incorporate intersectionality in your policy project, you need to look deeper at your datasets. Do you have data that can be disaggregated by sex, gender, ethnicity, housing, health, religion, or by other elements of identity? Disaggregating data in a robust and accountable manner enables better insights into the experiences of different groups.

Analysing intersectional data requires a high level of statistical skill. Regression analysis is one common method that allows researchers to look closely at different intersections. For example, using regression analysis could help you understand whether certain barriers affect women from some ethnic groups more than women from other ethnic groups.

Qualitative approaches

Intersectionality is about the distribution of opportunities, marginalization and systemic discrimination, not just demographics. This means that intersectionality is often explored through qualitative or mixed method research, with those who are most marginalized having a voice in the policy process.

Some key considerations when using qualitative approaches to gathering intersectional data include:

  • Engage with experts where you can. Intersectionality is complex and requires strong theoretical grounding and expertise.
  • Ensure diversity when recruiting research participants.
  • Ensure members of the group being surveyed have been consulted in the development and design of the research.
  • Use culturally sensitive methods of engagement.
  • Do not ask research participants to attempt to separate out aspects of their identity. Instead of asking “Do you think that your ethnicity or your gender influenced your experience of the recruitment process?”, ask “Do you think that your identity influenced your experience of the recruitment process?”
  • Approach the information you collect from different angles. For example, ask “How does the gender of the participant(s) inform what they are describing?” Then ask “How does gender interact with other equality areas (ethnicity, disability status etc) to inform what they are describing?”

Intersectionality is a thoughtful and skilled approach to data that requires expert advice and support. Engage with specialists in your economy to find the right skills to apply a thorough intersectional approach across your policy cycle.

Case study: Intersectionality and gender analysis in Canada (2020)

In British Columbia, “people with identities situated at various axes of difference display greater incidences and depths of material poverty, as well as encountering systemic barriers that limit opportunity, resiliency, and social inclusion and place them at greater risk of poverty throughout their lives.” Poverty rates are higher for people with identities at various axes of gender, race, age, disability status, indigeneity, and sexuality. Statistics, academic and social policy literature, and community and lived experience accounts confirm this.

Exploring the nature and causes of poverty in British Columbia revealed:

  • High rates of violence against indigenous women and girls
  • Young mothers in particular receive lower wages after having a baby
  • First Nations children are apprehended at a greater rate than non-indigenous children living in similar economic conditions
  • Indigenous single mothers and their children are among the most impacted by deep poverty
  • Queer indigenous women face a higher risk of social exclusion and a more fraught relationship with institutions when they seek support in meeting basic needs
  • Government systems and public institutions are complicit in reinforcing and perpetuating gender bias

This tells policy makers that their efforts to reduce poverty in British Columbia face a twofold challenge. Policy makers need to find ways to integrate the concept of intersectionality in a way that does not always prioritise gender as the primary focus of analysis. Policy makers also need to develop self-awareness and self-criticism, given the discrimination entrenched in government systems and other institutions.

Source: https://mpra.ub.uni-muenchen.de/105936/1/MPRA_paper_105936.pdf

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