Using intersectionality to understand structural inequality in Scotland: evidence synthesis
An evidence synthesis of literature on the concept of intersectionality. Looks at what the concept means, and how it can be applied to policymaking and analysis, as well as providing spotlight examples.
1. Key Findings
What is intersectionality?
- The concept of intersectionality, has long been used to articulate and analyse the lived reality of those who experience multiple and compounding inequalities, particularly within Black feminism. The analysis framework and term "intersectionality" was originally coined by American critical legal race scholar Kimberlé Williams Crenshaw in 1989, who used the term to refer the double discrimination of racism and sexism faced by Black women.
- There exists many different definitions of intersectionality in the literature, and academics have highlighted a lack of understanding of the central tenets of intersectionality.
- Looking across the various definitions in the literature, the key elements of intersectionality are:
- A recognition that people are shaped by simultaneous membership of multiple interconnected social categories.
- The interaction between multiple social categories occurs within a context of connected systems and structures of power (e.g. laws, policies, governments). A recognition of inequality of power is key to intersectionality.
- Structural inequalities, reflected as relative disadvantage and privilege, are the outcome of interconnected social categories, power relations and contexts.
Intersectionality in policymaking
The evidence included in this synthesis suggests that:
- An intersectional approach does not give higher status to any one inequality or experience of discrimination.
- Policymakers need to consider power dynamics and their own experiences and influence when making decisions.
- Evidence should be put in context, including the historical and contemporary structures of inequalities in wider society, and within local contexts.
- Currently in Scotland there is a lack of intersectional data on outcomes, and policymaking rarely takes an intersectional approach. Where an intersectional approach has been attempted, this could be developed further.
- A "one size fits all" approach to narrowing inequality leaves people behind, especially where multiple inequalities intersect.
- Too often a dilution or misappropriation of intersectionality is used which attempts to work "for everyone" and as a consequence ignores the specific and nuanced experiences of discriminations at the intersections of inequalities.
- The Poverty and Inequality Commission commissioned research that took an intersectional approach to understand lived experience of poverty. The research found that individuals with various intersecting protected characteristics experienced unique barriers that reduced their financial security and limited their ability to afford food and other essentials, including in accessing public services; digital poverty; and difficulties accessing and navigating the social security system.
- An intersectional approach was used to understand racial inequality during the COVID-19 pandemic. Data from Public Health England and National Records of Scotland showed that people from minority ethnic groups were more likely to contract COVID-19 and more likely to experience serious health outcomes. However, taking account of a range of factors, including socio-economic circumstance and reduced access to healthcare, demonstrates that the link between being a member of a minority ethnic group and a heightened risk of contracting and experiencing serious outcomes from COVID-19 is not direct.
- Research from Equate Scotland used an intersectional approach to understand the experiences of women working in or studying science, technology, engineering and maths (STEM) and the built environment. The research found that women with intersecting protected characteristics, including disabled women, LGBT women, minority ethnic women, women with caring responsibilities and women aged over 35, faced a number of different barriers in STEM, including discrimination and harassment.
- Research from Inclusion Scotland focused on the experiences of disabled people with intersecting protected characteristics when accessing services. The research was co-produced with disabled people, and found that disabled people with intersecting characteristics faced distinct barriers when accessing services – including a denial of choice, control and person-centred services – that they attributed, at least in part, to their intersecting identities.
Intersectionality for analysts
The evidence included in this synthesis suggests that:
- Analysts should pose critical questions throughout the research process. For example: who is included within this group? What role does inequality play, including the privilege and power experienced by the group? What are the similarities across groups that are often viewed as different?
- Analysts should practice reflexivity by asking themselves questions about their own social positions, values, assumptions, interests and experiences and how these can shape the research and data analysis design and process, as well as putting the research and statistical findings into context.
- Analysts should ensure that people at different intersections, particularly those from multiple marginalised groups, are included in research. Participatory approaches to research, when inclusively designed and implemented, can be a strong way to ensure that people with intersecting characteristics, in particular marginalised communities, are actively engaged in research from conceptualisation through implementation and dissemination. Such approaches view researchers and community members more equally, with each seen as having unique expertise.
- Statistical approaches to intersectional data analysis include: cross-tabulation analysis, which is what is currently most commonly used across the public sector in Scotland; interactions within multiple regression models; comparing multiple regressions run within different contextual variables, and multi-level models.
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