Where Numbers Fail: On numerical diversity and institutional intersectionality

‘You can’t be what you can’t see’, so the saying goes. This perspective elevates the status of ‘numerical diversity’, a concept that jars like glass in my throat as I can no longer see this diversity without thinking of its dangers. 

Many institutions – whether it is arts organisations, police forces, universities, health care providers or governments – have belatedly identified their role in building a fairer, more equitable society. But, too often, institutions respond to this challenge with an insufficient solution: numerical diversity. As an approach, numerical diversity imagines identity characteristics (woman, Black, disabled, gay, trans etc.) as something quantifiable that individuals bring to their roles, like a LinkedIn badge or an entry on a CV.

Our brains are hardwired to assume that when something is measurable it becomes easier to understand and therefore solve. For institutions, mirroring the wider world seems like a sensible solution: if 5% of the population identifies as LGBT+ then it feels logical that 5% of their workforce should identify as LGBT+. Yet, if an institution only intends to mirror the world around them, it is not doing enough to change structural factors that perpetuate the status quo. As an ambition, numerical diversity is low stakes and unlikely to change institutional structures in ways that improve the experiences of those most disadvantaged by current ways of working.

The most racially diverse leadership race in UK political history

My remaining embers of faith in numerical diversity were extinguished this summer during the jostling to become the next Prime Minister of the UK. A number of Black and Asian politicians announced their candidacy (Rishi Sunak and Kemi Badenoch, to name a couple) making it the most racially diverse leadership contest in UK political history. The roster of leadership candidates was reflective of Boris Johnson’s Cabinet and ministerial appointments, which were far more racially diverse than the UK electorate but hindered rather than helped efforts to address racial injustice (most notably, the 2021 Commission on Race and Ethnic Disparities report, which challenged the existence of institutional racism). The leadership race quickly descended into an ugly exhibition of anti-immigrant and anti-trans talking points, a distraction from the more pressing cost of living crisis, climate emergency, and COVID-19 pandemic.

We see a similar story when we consider the numerical diversity of LGBT+ Conservative MPs. 7.3% of Conservative MPs openly identify as LGBT+ (a higher figure than the estimated proportion of LGBT+ people in the UK population) but this ‘over-representation’ did not prevent the party from abandoning reform of the Gender Recognition Act, hollowing-out proposals to ban conversion therapy practices and reneging on many of its promises in the 2018 LGBT Equality Action Plan.

Institutional intersectionality

Numerical diversity sits uncomfortably with intersectional lives, those where a person’s experiences are shaped by multiple, overlapping identity strands. When quantified, intersectional experiences tend to produce numbers that are ‘too small’ or are unpublishable because they risk disclosing sensitive information about individuals. For instance, if you are the only Pakistani bisexual in your workplace, the problem of ‘small numbers’ means your employer cannot present quantitative data on your experiences without revealing to others that the data is about you.

More worryingly, when compared with population-level data, your presence might mirror (or even exceed) what one would expect to find in an institution of that size. When working with small numbers, the addition or removal of a single individual can make percentages jump up and down, painting an overly positive picture of the institution under investigation. In some sectors and industries, minoritised communities are numerically ‘over-represented’, such as LGBT+ communities (most often, gay men) in the arts. The nightmarish, though logical, next step for proponents of numerical diversity is a reduction of LGBT+ people in the arts or reduced funding for projects that target homophobia, biphobia and transphobia.

Numerical diversity is not enough

My research and writing focus on LGBTQ lives in the UK. But I don’t want more gay male faces in high places when those in power are a pawn to heteronormative and patriarchal interests. There is a broken link in the chain that joins numerical diversity and building a better society: someone who is a Person of Colour is not always an anti-racist, just as someone who is LGBT+ is not always opposed to homophobia, biphobia and transphobia.

One solution is a more intersectional approach to numerical diversity. If institutions look beyond mirroring the world and dedicate more attention to the intersecting forces of gender, race, sexuality and social class – for example, adopting methods that embrace messy categories – we might see less celebration of ‘diverse’ individuals who champion political ideas that inflict harm on their communities. Numerical diversity is not enough. The contingent and contextual concept of ‘diversity’ is not an endpoint – it is only a stepping stone to something bigger.

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Kevin Guyan (@kevin_guyan) is a researcher and writer who investigates the intersection of data and identity. He is the author of Queer Data: Using Gender, Sex and Sexuality Data for Action (Bloomsbury Academic), which explores data about LGBTQ+ people in the UK and is published in paperback.