Before I provide statistical ‘evidence’ of structural inequality in the international academy, I discuss the important role statistics offer educational policy makers and practitioners. Even though I first consider their limitations, I offer a detailed rationale for both gathering and employing large scale data captured in statistical form.
All statistics share some limitations. When decontextualised they cannot determine or communicate the complexity of gendered, raced, classed, and ableist structures and cultures of organisations. They also fail to communicate the intersectional disadvantage experienced by people who identify as ‘woman’, transgender, Black, Asian, minority ethnic, Indigenous, working-class, differently abled, et cetera; and/or the compounded disadvantages people from other marginalised locations people inhabit. Statistics might also create emotional distance between an audience and the phenomenon presented as they can mask or even obscure significant contextual factors or impacts, and masquerade as a positivist ‘truth’. As a result, they can fail to evoke the lived experience of people represented as a ‘number’ or ‘percentage’ and diminish the felt experience of people’s location/s. Furthermore, statistics often communicate and thereby further entrench dichotomies of ‘difference’ such as male/female, which can cause further exclusion/s and symbolic violence against gender diverse people. And not every aspect of a given population has been captured by statistics, as ‘there is always an implicit choice in what is included and what is excluded’ (Davies, 2017). For instance, as a feminist, I am cynical (read: angry) that Gross Domestic Product (GDP) only captures the value of paid work and excludes the domestic work traditionally/most often undertaken by women. Similarly, Davies (2017) identifies that in France it is illegal to collect census data because it is feared that such data might be used for racist political purposes even though a by-product of this exclusion is a difficulty in quantifying the systemic racism in the labour market.
Nevertheless, we need to be careful not to dismiss the use of statistics because this could bolster what some political analysts have identified as the anti-intellectual manoeuvre by the far-right to discredit statistical data and analysis in favour of personality politics (Motta, 2018). It could also render invisible significant societal inequities, and the structures that create or propagate them.
Davies (2017) identifies a growing cynicism towards statistics in many societies, and the political schism of such mistrust. For instance, shortly before the US November 2016 presidential election 68% of Donald Trump supporters noted a distrust for the economic data published by the then federal government; this compared with the 40% of distrusting respondents who did not identify as Trump supporters (Rampell, 2016). Similarly, politically conservative or right-leaning Republicans in the US, and Republicans who watch Fox News, are more likely to distrust statistics about rising temperatures, and their causes and impacts, than people who identify as Democrats (Matthews, 2017). An analysis of these studies reveals that people with right-wing political views tend to assume that statistics are manipulated and dislike what is considered their abstract and elitist form. This leads to a decline in the authority of statistics and a vilification of the experts who present them (Davies, 2017).
A contrasting position is that statistics can offer easy to understand information which enable researchers, citizens, and politicians to understand and discuss society ‘as a whole’, and in ways that can be validated. Statistics can indicate levels of health, prosperity, equality, and whether certain policy development makes things better or worse for groups or sub-groups of society. And here-in lies the main benefit of statistics in relation to this collection of essays: statistics can help to provide information about who is, and who is not, included in various levels of academia; information which can be used to directly address any identified areas of under-representation/s, and help to shape education or institution policy development. Statistics can thus provide impetus and political will (and financial and human resource) to address inequity.
However, statistical data is only credible if people accept the limited range of demographic categories that are on offer (Davies, 2017). I acknowledge that many of the statistics I present to demonstrate structural inequity in academia are based on traditional, and outdated/exclusionary categories of male and female. I concede their limitation/s. Many of the statistics are also based on the Global North, which may suggest/propagate a narrowed ontological lens. I nevertheless use them because they are readily available and illuminate gendered inequity both in faculty and student representations within various levels of seniority and disciplinarity in academia. However, I will (and encourage us all) to include non-binary persons as a category in surveys and other means by which we collect data from now on, and to keep searching for data that extends the international boundaries of the Global North. Now that we know better, we must do better.
The next section of the chapter will therefore present a summary of statistical data that collectively determines the systematic exclusion of staff and students within higher education based on lines of ethnicity, social or socio-economic status, gender, and disability. Dispiriting as the statistics may seem, we can use them to inspire equity strategy and intervention: we need to see landscape to know where our energies, creativity and activism is needed.
Statistics of Structural Inequity
Inequity Based on Race: Faculty Data
Numerical and statistical data reveals structural and systemic under-representation of Black, Asian and minority ethnic (BAME) staff in universities in the UK, Australia, and the US. This is highly problematic because students need to see ‘people like them’ in senior and leadership roles in order to aspire to leadership roles themselves, and need leaders from a diversity of experience and location to inform university curricular (in its broadest sense, including knowledge and knowledge systems) (Crimmins, 2019). Consequently, an under-representation of people with diverse ethnicities in senior roles serves to propagate existing inequities and straightjackets conceptions of knowledge.
When Black, Asian and minority ethnic people are employed in British Higher Education Institutions (HEIs) they are over-represented in lower academic ranks and non-academic roles, and under-represented in senior levels of academic employment. Out of the 145,560 people (UK nationals) employed at British universities in 2017–2018, 7480 identified as Asian and 2040 as Black, and the vast majority of BAME staff occupied technical, administrative or lower-level academic positions (Higher Education Statistics Agency, 2019b). In addition, out of the 1350 senior managers (including all highest levels of university management) only ten identify as Asian, and none as Black (Henry et al., 2017). Concomitantly, specific ethnic groups are also under-represented amongst the professoriate. Only 0.4% of the UK professoriate are Black, compared to 11.1% who identify as White (University and College Union, 2013). Yet the most startling fact emerging from the data is that the vast majority of HEIs have so few non-white professorial staff that pay gap data is not available. For instance, not a single HEI has data relating to the pay of Black professorial staff which indicates that no UK HEI has more than seven Black professorial staff. The statistics again reveal that the ethnicity of large numbers of staff are unknown. Yet where data is provided, significant pay gaps are revealed. In England, statistical data reveals that professors of Black, Chinese and other ethnic origins earned between 9.7% and 3.6% less than their White colleagues. In Wales, insufficient data for Black professors is available yet Chinese professorial staff earned 7.5% less than White colleagues. Finally, in Scotland, Black professorial staff earned 9.9% less than White professorial staff, Chinese professors earned 10.2% less, and professors from other ethnicities, including mixed race professors earned 7.5% less than their White colleagues (University and College Union, 2013).
Similarly, and even though Asian Australians are the fastest growing minority group in Australia constituting 14.4% of the population in 2016 (Australian Bureau of Statistics, 2017), only 3.4% of Deputy Vice-Chancellors were Asian-born in 2015, and there are currently no Asian-born Vice-Chancellors within Australian universities. This is in stark contrast to other overseas-born academics where 33% of Deputy Vice Chancellors and 25% of Vice-Chancellors were born overseas (Oishi, 2017). Yet most Vice-Chancellors in Australia have an Anglo-Celtic background (82.5%) or a European background (15%). Furthermore, there are no Vice-Chancellors with an Indigenous background (Soutphommasane, 2016) despite that 3.3% of Australian society identifies as Aboriginal and Torres Strait Islander (Australian Bureau of Statistics, 2017). Indeed, Indigenous people are so dramatically under-represented as employees of Australian universities at all levels that population parity in academic and general staff representation is not achievable by any pipeline effect. Staff numbers in ‘teaching’, ‘research and teaching’ and other general positions would need to increase by a factor of between two to three to reach population parity, while staff numbers in ‘research only’ roles would need to increase by a fact...