The one-in-ten: quantitative Critical Race Theory and the education of the ‘new (white) oppressed’

ABSTRACT

This paper challenges the notion that quantitative data – as a numeric truth – exist independent of a nation’s political and racial landscape. Utilising large-scale national attainment data, the analysis challenges the belief that ‘White working class’ children in England, especially boys, are ‘the new oppressed’ – as a former equality adviser has publicly claimed. The analysis applies Quantitative Critical Race Theory, or ‘QuantCrit’, an emerging quantitative sub-field of Critical Race Theory in education. The paper argues that far from being ‘oppressed’, White boys continue to enjoy achievement advantages over numerous minoritised groups; especially their peers of Black Caribbean ethnic origin. Additionally, the analysis uniquely exposes racialised trends of ‘equivalency’ in core subject qualifications, whereby minority ethnic children are over-represented in certain lower-status qualifications that are counted as equivalent in education statistics but not in the real world labour market. The analysis concludes that knowing misrepresentations of quantitative data are at the heart of an institutional process through which race and racism are produced, legitimised and perpetuated in education.

KEYWORDS:

• Critical Race Theory
• CRT
• quantitative research methods
• white working class
• achievement gap
• education policy
• racism

Introduction

Quantitative research in education, and quantitative inquiry more broadly, is largely unaccepting of the idea that we should be explicit about the biases that researchers bring into the research process. Despite becoming commonplace practice for qualitative researchers – who serve as the ‘instruments’ in ‘data collection, analysis, interpretation’ and presentation – much quantitative research continues to trade on an image of objectivity, detached and ‘free of politics’ in the pursuit of numeric truth (Carter and Hurtado 2007, 33). Education, however, is not free of politics, and education research does not happen in a vacuum; from its design and funding, to its administration and presentation, it would be a gross mistake to believe quantitative data exists independent of political debates or the social location of the researchers and their audiences (Carter and Hurtado 2007; Zuberi and Bonilla-Silva 2008). Therefore, as a researcher who utilises Critical Race Theory (CRT) as both a framework and method to conduct critical examinations of official data, I start by acknowledging that quantitative research is not objective (Blair 1998; Garcia, López, and Vélez 2018) and that racism is deeply entrenched in the fabric of a nation’s institutions, and by association, within its official reports, statistics and dominant truth claims.

This paper begins by providing an overview of the current political positioning of White victimhood in the British education and policymaking climate. The theoretical and methodological orientation of the analysis are then outlined before illustrating how the ‘White working class’ have been discursively constructed as a distinctly disadvantaged racial group under siege. The paper then conducts a critical race informed examination of quantitative attainment data for 14–16 year olds in England; exploring the findings in three distinct subsections. First, I examine how an image of the ‘White working class’ has been constructed based on misleading and highly selective quantitative data. Second, I show how the official reports and popular media coverage actively encourages a sense of siege in the English school system, by focusing on attainment data pertaining to ‘one-in-ten’ White students and drawing attention away from the remaining ‘nine-in-ten’. Third, I explore the processes by which schools are permitted to utilise so-called ‘equivalent qualifications’ to bolster the apparent performance of their minority ethnic students in the published attainment data while concealing the lower-status of the contributing qualifications. The paper concludes that – despite the powerful image of White students as ‘the new oppressed’ in English schools (as per the media and official discourses on educational under-achievement), in fact, children in particular minoritised ethnic groups continue to bear the brunt of racialised systems of oppression – especially those with family origins in the Black Caribbean.

Although the analysis focuses on the English national context, the theorisation, critical analysis, and questions raised about how numbers are deployed in government education policy (and resulting attainment data) have wider relevance beyond national boundaries. For example, the analysis builds upon and expands the work of Australian scholars, who have called for a critical awareness of the socially constructed nature of categories that underpin contemporary policy-as-numbers (Ford 2013; Lingard, Creagh, and Vass 2012, 2016a, 2016b), and American scholars, who are examining the extent to which quantitative methods might extend a critical race agenda in educational research (Covarrubias et al. 2018; Garcia, López, and Vélez 2018; Pérez Huber, Vélez, and Solórzano 2018).

Creating a siege mentality

‘BETRAYAL OF WHITE PUPILS’ (Daily Mail, 4 April 2016)

So screamed the front-page headline of Britain’s most politically influential, and best-selling, daily newspaper (Martins-Ojo 2016; Ponsford 2015). Although striking in its directness, and incendiary tone, the headline was far from unique. In fact, this was merely the latest in a long line of prominent news stories, stretching back to the mid-2000s, that repeatedly proclaim that White Britain is under attack; under siege by minority ethnic groups whose presence is said to cause specific damage to the White-British working class (hereafter ‘White working class’) (Gillborn 2010b). The field of education has consistently been among the most prominent policy areas to be shaped by these discourses of White racial victimhood. Recent headlines have included, ‘White British pupils “lag behind ethnic minority peers”’ (BBC, 2016); ‘Treat White-working class boys like ethnic minority’ (Independent [Garner 2013]); ‘White British children outperformed by ethnic minority pupils’ (Guardian [Press Association 2013]); ‘Pupils with English as second language “outperform” White British at GCSE’ (Telegraph [Espinoza 2016]); and, ‘Give White working-class children extra English to help catch up’ (Guardian [Weale 2015]).¹

The sense of the White working class being under siege in Britain’s schools has been further strengthened by official calls for schools, local authorities and Ofsted (the schools inspectorate) to explicitly target ‘White working class’ children for special support (Education Select Committee 2014). In August 2016, Prime Minister Theresa May launched the government’s plans for an audit of public services to ‘examine the disparities’ in the way that some individuals and groups are treated by government departments and public services (May 2016).² May stated that the data collated would give ‘every person the ability to check how their race affects the way they are treated by public services.’ According to the Prime Minister, this ‘transparent information’ will also help the government and the public more broadly to ‘force poor performing service’ to improve and ‘will show disadvantages suffered by White working class people as well as ethnic minorities’ (May 2016).

May’s positioning of the ‘White working class’ alongside disadvantaged minority ethnic groups is highly significant. The claim that White ‘working class’ people in Britain (to the exclusion of other working class groups) are a distinctly disadvantaged racial group is politically powerful and defines the context for the analyses in this paper. Before considering the data in more detail, however, it is necessary to set out the analysis’ theoretical and methodological underpinnings.

Sources and statistics

The analyses draw on a range of official statistics generated by the UK government as part of its annual monitoring and reporting mechanisms: including Statistical First Releases (produced by the Department for Education), the National Pupil Database (Department for Education) and material from the newly launched ‘Race Disparity Audit’ website (published by the Cabinet Office). The data presented in this paper pertain to the 2010–2011 and 2015–2016 academic years, a period that witnessed a distinctive change in the UK’s political direction. Briefly; in 2010, the newly elected Conservative-Liberal Democrat coalition³ took power with a promise to overhaul the education system in favour of new ‘gold standard’ ‘rigorous’ qualifications (see DfE, 2015). By 2016, the now majority Conservative government, outlined its vision for schools in the white paper: Educational excellence everywhere (DfE, 2016). Heralding its successes since 2010⁴, the paper delivers an explicit promise to focus on ‘outcomes’ and ‘set high expectations for every child, ensuring that there are no forgotten groups or areas’ (Department for Education (DfE) 2016, 3).

The National Pupil Database (NPD) is a longitudinal pupil-level database linking pupil and school characteristics to attainment information. The NPD data presented in this paper pertains to the 2010/11 school year and includes all children in maintained schools⁵ in England, registered for ‘Key Stage 4ʹ examinations. Key Stage 4 (hereafter KS4) is the official designation for the final two years of compulsory-age school education in England, which incorporates the General Certificate of Secondary Education (GCSE) examinations, and other equivalent assessments, ordinarily taken when students are aged between 14 and 16. Although some general analyses of NPD are made publicly available in official reports, specific access to the database – which is needed to explore the data in more detail – required permission from the Department for Education.

This analysis also utilises a Statistical First Release report (DfE, 2011) which combines information gathered through the ‘School Census⁶’ in January 2011, and the 2010/11 Key Stage 4 attainment data taken from the NPD. The report analyses the number and percentage of pupils achieving various outcomes at the end of KS4 by pupil gender, ethnicity, English as a first language, eligibility for Free School Meals (FSM), and Special Education Needs (SEN).

Finally, the analysis draws on the most recent round of data released from the government’s ‘Race Disparity Audit’ (Cabinet Office, [October] 2017). Trumpeted as an assault on racial injustice (Conservative Party, 2017), the audit holds information on a variety of public sector services⁷, including ‘Education, skills and training’ pertaining to the 2016-2017 school year. The audit is a publicly accessible database which claims to help users ‘understand and assess differences between ethnic groups’ and ‘identify those public services where disparities are diminishing and those where work is needed to develop effective strategies to reduce disparities between ethnic groups’ (Office 2017). Academic performance data contained in the education section of the database can be analysed by race, ethnicity, gender, and FSM eligibility.

Collectively, these official sources (the NPD, Statistical First Release and Race Disparity Audit) offer a comprehensive snapshot of the racialised profile of student attainments and outcomes in state schools in England.

Race-ing numbers: Quantitative Critical Race Theory (Quantcrit)

With its origins in U.S. law schools in the 1970s, Critical Race Theory (CRT) has developed to become one of the most important academic perspectives on racism within the field of education, making significant contributions on both sides of the Atlantic (see Dixson and Rousseau 2005; Gillborn 2008; Hylton et al. 2011; Ladson-Billings 1998; Ladson-Billings and Tate 1995; Ledesma and Calderón 2015; Leonardo 2009; Parker and Lynn 2006; Parker and Stovall 2004; Tate 1997). The limits of space preclude a detailed overview of CRT here, suffice to say that the perspective views racism as a subtle but extensive feature of contemporary societies; a factor that saturates the everyday routine of daily life to such an extent that racist practices and assumptions – which privilege White racial interests at the expense of minoritised groups – frequently appear ‘ordinary and natural to persons in the culture’ (Delgado and Stefancic 2000, xvi).

CRT challenges the traditional claims of the education system and its institutions to objectivity, meritocracy, color and gender blindness, race and gender neutrality, and equal opportunity (Solórzano 1998, 122). The vast majority of CRT adopts qualitative approaches, typically drawing on interview data, auto/biography and narrative forms, that sometimes blend empirical examples with invented or composite characters to create counter-stories (Delgado 1993; Yosso and Solorzano 2006; Dixson and Rousseau 2005; Parker and Lynn 2002). Scholars such as Carbado & Roithmayer (2014; see also Obasogie, 2013; Gómez, 2012) have argued that CRT should engage more directly with mainstream social science methodology to advance core CRT claims – but acknowledge the benefits and the costs to CRT scholarship in collaborating with more mainstream social science (Barnes 2016).

To date, relatively few critical race scholars have explored the use of quantitative resources. In legal studies, Barnes (2016), Kimani (2015), and Obasogie (2013), have explored ‘eCRT’ (CRT and empirical methods) as a fruitful line of inquiry and advocated ‘the marshaling of empirical evidence to support theoretical, doctrinal, or normative claims and the production of qualitative or quantitative empirical data informed by CRT’ (Kimani 2015, 2957). In education, scholars have theorised the necessity of critically informed models of quantitative research and have called for a better understanding of how quantitative methods are frequently mobilised in uncritical ways that produce racial ‘knowledge’ that operates to the advantage of dominant White interests (see Covarrubias 2011; Covarrubias and Velez 2013; Gillborn 2010a; Zuberi & Bonilla Silva, 2008; Zuberi 2001). In an attempt to build on these earlier treatments, while more clearly linking back to the founding principles of CRT, Gillborn, Warmington, and Demack (2018) introduced QuantCrit – or ‘Quantitative Critical Race Theory’ (see also Garcia, López, and Vélez 2018). They propose five principles that can be employed to guide quantitative race critical scholarship and sensitize scholars to the multiple and often hidden ways in which racialised expectations and assumptions can (sometimes unwittingly) shape, and be reinforced by, quantitative research:

1. Centrality of racism – a foundational principle of CRT is that race is ‘more than just a variable’ (Lynn and Dixson 2013, 3) and that racism is ‘a complex, fluid and changing characteristic of society,’ not readily acquiescent to ‘quantification’ or ‘statistical inquiry’ (Gillborn, Warmington, and Demack 2018). Racism is a relational quality of human interaction that cannot be simply or obviously identified as a discrete ‘thing’ to be counted and measured. This means that racism will rarely be obvious in statistical analyses and may even be obscured from view in the apparent workings of other factors that traditional analyses might assume to operate independently of race.⁸

2. Numbers are not neutral – quantitative data tends to be gathered and analysed in ways that reflect (and therefore protect) the interests, assumptions and perceptions of White-dominated institutions. All statistical treatments must, therefore, be interrogated for ways in which majoritarian assumptions might have unwittingly shaped the collection and analysis of ostensibly ‘objective’ quantitative data.

3. Categories are neither ‘natural’ nor given – this principle refers to the recognition that complex, historically-situated, and contested terms (like race and dis/ability) are normalised and mobilised as labeling, organising, and controlling devices in quantitative research and measurement. The categories that shape quantitative research may themselves be implicated in the processes that create, and disguise, race inequity; the choice of terms and where group boundaries are drawn, therefore, are difficult questions that should be interrogated for possible unrecognised and unintended consequences.

4. Voice and insight: Data cannot ‘speak for itself’ – quantitative data is open to numerous, often-conflicting interpretations. There is no single ‘correct’ understanding of social statistics and, so far as possible, the narratives of minority ethnic groups (in the form of experiential knowledge – as intersected with gender, sexuality, class, and dis/ability) should help to inform the analysis of quantitative research data.

5. Numbers for social justice – the final QuantCrit principle describes a commitment to use quantitative data as an anti-oppressive praxis, to support social justice and challenge dominant narratives that usually treat race as a marginal or specialist concern.

Utilisng the principles of QuantCrit, the remainder of this paper sets out a critical examination of the claim that the ‘White working class’ are a distinctly disadvantaged racial group within the English education system; starting with a critical analysis of the labels being operationalised in dominant treatments of ‘White working class’ educational attainment.

The ‘white working class’ as a distinct and disadvantaged racial group

The continual reiteration of ‘the White working class’ in English political discourse, and educational policy in particular, has promoted a situation where this ethnic and class fraction are treated as akin to a victimised racial group in its own right (see Gillborn 2015; Warmington 2009). A sense of siege in Britain’s schools is reinforced frequently, and in the most high profile ways. For example, in her very first speech as the new prime minister, Theresa May stated: ‘If you’re a white working-class boy, you’re less likely than anybody else in Britain to go to university’ (May 2016). In fact, White British students are several times more likely to enter education than their peers of Gypsy, Roma and Traveller backgrounds – who are recognized as a minority ethnic group in UK law (Morley et al, n.d; Mulcahy et al. 2017). May was not the first Conservative politician to erroneously make this claim; a former Universities Minister called for the Office for Fair Access (OFFA)⁹ to look at White working class boys ‘the same’ as ethnic minority groups (Garner 2013), arguing that ‘it is a scandal that ethnic minority kids are more likely to go to university than poor White ones’ (Telegraph [Kirkup 2015]). The Director of Fair Access agreed, claiming the low participation of young White men from disadvantaged backgrounds, specifically, is a ‘shocking, and avoidable, waste of talent’ (OFFA, 2016).

As noted earlier, the media are equally persistent in their racialised narrative. Whether left, centrist or right in political alignment, the distinct racialised category ‘White working class’ is repeatedly deployed to present the group as innocent victims of unfair racial competition, in that the group are being ‘outperformed’ and ‘overtaken’ by other minority ethnic groups. Despite the term’s pervasive nature, however, there is rarely any discussion of what ‘working class’ actually means in this context. It seems to be taken for granted that voters, politicians, academics and journalists all understand the term with perfect clarity. Unfortunately, this is far from the case and serious errors of meaning and interpretation arise from this situation.

In contemporary social science research in the UK the term ‘working class’ is usually associated with specific categories based on a combination of income and employment/occupational rankings, most notably as categorised in the Office for National Statistics’ (ONS) National Standard Socio-Economic Classification (ONS, 2005; Savage et al. 2013). These formal classifications require detailed information about specific occupations, often including the level of responsibility and autonomy involved in particular roles. Such detail is difficult and costly to reliably collect and so most official statistics do not include detailed socio-economic data. Typically, official statistics rely on much cruder data that are easier, and cheaper, to collect.

In UK education statistics, for example, a child’s eligibility for free school meals (hereafter FSM) is widely used as a crude proxy for social disadvantage; and in numerous accounts, FSM eligibility is used interchangeably with the term ‘working class’. Therefore, government and media published statements relating to the so-called ‘underachievement’ of the White working class are almost exclusively based on achievement data for those White children receiving FSMs¹⁰. Broadly speaking, to be eligible for FSMs in the UK, a child must reside in a household where no one is employed, or is not employed for more than 16 hours a week and receives a low income (defined relative to national standards), with only limited capital assets.

Table 1 shows the percentage of students, attending state funded schools, receiving FSMs in the principal ethnic groups as defined in UK official statistics¹¹. As a percentage of the total, it is clear that White children have some of the lowest FSM claimant rates. In 2011, 11.5 per cent of White children claimed FSMs during their final year of compulsory education. Black Caribbean children were more than twice as likely to claim FSM (23.5%), Black African and Pakistani students approximately three times more likely (35.1% and 30.4% respectively), and Bangladeshi students almost four times as likely (44.6%). Therefore, when the government and media refer to the ‘White working class’ – or the underachievement thereof – they are more accurately referring to the performance of the roughly one-in-ten White children who claimed FSMs during the final two years of compulsory-age education.

Table 1. Free school meal eligibility by race/ethnicity (KS4, 2010/2011).

Group	Number of students claiming FSM	Total number of students	% of group population
White British	50,976	443,555	11.5%
Indian	1,233	12,948	9.5%
Pakistani	4,997	16,423	30.4%
Bangladeshi	3,099	6,950	44.6%
Black Caribbean	1,894	8,058	23.5%
Black African	5,069	14,431	35.1%
Chinese	162	2,303	7.0%

Source: National and Local Authority Tables SFR03/2012 (Table 2(a))

Table 2. Students claiming free school meals and achieving 5+GCSEs at grade A*-C by gender & ethnic origin (KS4, 2010–2011, percentages).

	% Success Rate 5+GCSEs at Grade A*-C
Group	Male	Female	All
White British	54.8	64.6	59.6
Indian	75.2	85.9	80.5
Pakistani	71.0	78.6	74.6
Bangladeshi	76.8	84.5	80.7
Black Caribbean	64.6	76.5	70.7
Black African	73.2	78.3	75.8
Chinese	81.9	94.9	88.3

Source: National and Local Authority Tables SFR03/2012 (Table 2(a))

Table 2 outlines the percentage of students claiming FSMs that have successfully met one of the government’s key attainment benchmarks, achieving ‘five or more GCSE’s¹² graded A* to C’. If we focus attention solely on the performance of students eligible for FSMs, the table demonstrates that of all reported categories, White British children are the lowest performing group; with approximately one-in-two White males, and two-in-three White females, succeeding in achieving the benchmark ‘5+GCSE A*-C’. White males underachieved by between 10 and 27 percentage points compared to their minority ethnic peers (in the sense they were between 10 and 27 percentage points less likely to achieve the benchmark).

Although White females were approximately 10 percentage points more likely to achieve the benchmark than White boys, their underperformance was marked when compared to other minority ethnic groups. White females underachieved by between 12 and 30 percentage points, and again, were less likely to succeed than the historically lowest performing principal ethnic group – children of Black Caribbean ethnic heritage.

It appears to be clear, therefore, that in the benchmark of achieving five or more GCSEs graded A*-C, White British children claiming FSMs were at the bottom of the racialised performance spread in 2011 when comparing the main minority groups – Gypsy, Roma and Traveller children are frequently absent from official data¹³ and, as noted above, seem to disappear from view when policy-makers make claims about the ‘white working class’ (Morley et al., n.d.). However, we must proceed with caution. QuantCrit urges researchers to remember, that ‘ethnic origin’, as a government reported category, is neither a fixed nor pre-determined characteristic that can be employed to make generalised assessments of a child’s capacity independent of wider racialised social relations. Equally, the performance of one racially minoritised group, tells us nothing about the mechanisms and causality for another – racisms operate differently for different groups. As Dumas and ross argue: ‘Black struggle is inherently and always a coalition of Black people with different social location, across boundaries of gender, but also social class, sexuality, and other differences’ (Dumas and ross 2016). Thus government collected and generated assessment data, such as those illustrated here, must be treated with caution when presented as ‘evidence’ to lend support to racially loaded and classist logics within political agendas. Similarly, we should be wary when analyses seem to embody the notion of typical or ‘expected’ achievement defined by racial group (Bradbury 2011a, 2011b). Thus, when a single measure of performance alone (e.g. ‘5+GCSE A*-C’) is taken as trustworthy and robust evidence that White working class children ‘lag behind’ (B.B.C 2016); are being ‘outperformed’ by their minority ethnic peers (Guardian [Press Association 2013]); or are in a school system that unfairly favours children of minority ethnic backgrounds (e.g. ‘Treat White-working class boys like ethnic minority,’ Independent [Garner 2013]), we should interrogate the assumptions and hidden definitions that lie behind the claims.

Applying a critical understanding of the use and deployment of FSM data, the remainder of this paper will: 1) illustrate how FSM data, as a damaging proxy for the working class, creates, entrenches and perpetuates the sense of an ethnic group under siege; 2) examine the performance of the nine-in-ten White British children who were not claiming FSMs; and 3) critically question the benchmarks applied to KS4 attainment data claims and explore the potential for hidden inequities in the relative value of the ‘equivalent’ qualifications that make up the statistics.

Free school meals: a dangerous proxy for ‘working class’ identity

This section offers a QuantCrit perspective on how the racialised label ‘White Working class’ is utilised and damagingly mobilised in UK government statistical publications.

According to the British Social Attitudes Survey (NatCen 2015), some 60 per cent of British adults consider themselves to be ‘working class’. This is highly significant, especially in view of the government’s use of the label ‘White working class’ in combination with the selective use of performance data on the approximately one-in-ten White children eligible for FSMs [Table 2]). This siege narrative is bolstered by the media and speaks robustly to the 60 per cent of White individuals who consider themselves (and their families) to be working class. The deliberate use of FSM data as a dangerous proxy for the working class has very real implications for public debate about race and education. The 60 per cent of adults who consider themselves ‘working class’ might reasonably assume, for example, that talk of ‘White working class’ failure relates to a majority of white students, not the one-in-ten actually referenced in FSM data. Indeed, the Education Select Committee acknowledged this issue when faced with evidence on the mismatch between the meaning of ‘working class’ in common usage as opposed to official statistics: ‘The logical result of equating FSM with working class was that 85% of children were being characterised as middle class or above’ (HCEC, 2014, 8). Nevertheless, the Select Committee chose, for the sake of ‘pragmatism’ (p. 10), to continue with the misleading practice. Indeed, their report itself was titled; Underachievement in Education by White Working Class Children (House of Commons Education Committee (HCEC) 2014).

The problem was compounded when a more recent report from the Department for Education continues the fallacy; citing the Select Committee’s report as its authority on the matter: ‘In this report the term “White British” is used to refer to all pupils in this ethnic category, while “White working class” refers to White British pupils who are eligible for free school meals, following the approach used in the Education Select Committee report’ (Stokes et al. 2015, 5).

The principles of QuantCrit prompt us to critically interrogate the type and significance of data labels presented in publications, i.e. troubling the labels or categories selected (or excluded), questioning how labels are operationalised, and critically examining how data are grouped and/or presented. Thus, in this paper, the government and associated official agencies’ continued commitment to operationalising deeply erroneous and racially loaded data labels in their publications must be troubled; as evidenced in the Select Committee’s decision to continue to use the misleading label regardless of the evidence to the contrary – this is not an innocent misunderstanding, nor accidental oversight: the Select Committee acknowledged the potential dangers but then went on to repeat the errors without further comment (thereby adding further authority to the key assumptions).

The white working class. It’s a phrase that has become so commonplace that few recognise the sheer oddness, and indeed odiousness, of the concept. It denotes both pity and contempt. On the one hand, it is a description of the “left behind”, sections of the population that have lost out through globalisation and deindustrialisation. On the other, it is shorthand for the uneducated and the bigoted, people who support Donald Trump or Brexit, and are hostile to immigration and foreigners. (Malik 2018)

As Kenan Malik has noted, the label ‘White’ when combined with ‘working class’ speaks powerfully to anti-immigration, nationalist, and racist sentiments that are ever present in contemporary Western democracies (cf. European Union Agency for Fundamental Rights 2017). In the academy, the selective use of data framed by racialised and/or classist labels are often deployed in ways that silence important debates and suppress vital research findings; particularly when it comes to discussing institutional racism (see Ziliak and McCloskey 2008). For example, the categories ‘BME’ or ‘BAME’ [Black and Minority Ethnic; Black, Asian and Minority Ethnic], as commonly utilised in British academe, have great potential to subsume the varied experiences of all whom are ‘not White’ under one large and relatively meaningless category; and can ultimately give a grossly false sense of progress for some minority ethnic groups (Bhopal 2016). For many commentators – including ostensibly progressive voices such as Malik (above) – quantitative ‘evidence’ such as the data in Table 2, are all too often taken as confirmation of a phenomenon, without subjecting the data to critical analysis. When researchers, politicians and the media fail to critically engage with the racialised nature of statistics, there is great potential to fuel the focus on class to the exclusion of race (see also Cole 2009; Cole and Maisuria 2007; Hill 2009). As I have noted above, this can be as dramatic as taking data that describes around one in ten of the relevant population (i.e. FSM White British students) and presenting it as if it relates to two in three (i.e. the 60% of the population who think of themselves as ‘working class’).

‘White British’: the performance of the hidden nine-in-ten

In this section I focus on the performance of the nine-in-ten White students that were not claiming FSMs in 2011. Essential to creating a sense of siege is to ignore the minority groups who perform worse than their White peers in government assessment data. Table 3 re-presents data from Table 2 for those claiming FSMs in the first column but with the addition of the performance data of those not claiming in the second column, i.e making visible the otherwise hidden nine-in-ten.

Table 3. Students achieving 5+GCSEs at grade A*-C by FSM-status, gender & ethnic origin (KS4, 2010–2011, percentages).

	% Success Rate 5+GCSEs at Grade A*-C
	Claiming FSM			All Other Students			All Students
Group	M	F	All	M	F	All
White British	54.8	64.6	59.6	79.6	86.3	82.9	80.2
Indian	75.2	85.9	80.5	88.8	93.5	91.1	90.1
Pakistani	71.0	78.6	74.6	80.1	86.5	83.1	80.5
Bangladeshi	76.8	84.5	80.7	81.5	87.6	84.5	82.8
Black Caribbean	64.6	76.5	70.7	74.5	84.5	79.5	77.5
Black African	73.2	78.3	75.8	82.4	88.2	85.4	82.0
Chinese	81.9	94.9	88.3	91.3	95.0	93.1	92.7

Source: National and Local Authority Tables SFR03/2012 (Table 2(a))

When we examine the performance data for those ‘Claiming FSMs’ in isolation, the lowest performing group of the largest ethnic groups are White students, both male and female. What is important to remember, however, is that this statistic remains only true for one-in-ten White students. When we examine the performance of those not claiming FSMs against the same benchmark (‘All Other Students’), White British students are not the lowest performing group; Black Caribbean students are. The same is true when we examine the rate of success for the student population as a whole (‘All Students’). Thus, in terms of those achieving 5 or more GCSEs graded A* to C, as a group, nine-in-ten White British children – both male and female – outperform their Black Caribbean peers; posing a direct challenge to the potentially inflammatory headlines such as ‘White British pupils “lag behind ethnic minority peers”’ (B.B.C 2016).

Furthermore, when we consider the performance data for the nine-in-ten White non-FSM children, the racialised gaps identified in the previous section are either eliminated altogether or significantly reduced. For non-FSM White males, the gap reported in the previous section is eliminated altogether – in the case of Black Caribbean males – and reduced by approximately 8 to 15 percentage points (pp)¹⁴. In relation to those achieving five or more GCSE’s graded A*-C, as a group, non-FSM White males outperform their Black Caribbean peers; near equal the performance of their Pakistani counterparts (+0.5pp); and underperform (but to a lesser extent) compared to their Bangladeshi (+1.9pp), Black African (+2.8pp), Indian (+9.2pp), and Chinese (+11.7pp) peers. As a group, for non-FSM White females the success gaps are again either eliminated – in the case of Black Caribbean females – or reduced by approximately 11 to 21 percentage points¹⁵. In the relation to those achieving ‘5+GCSEs A*-C’, White females outperformed their Black Caribbean peers; were equally likely to succeed as their Pakistani counterparts (+0.2pp); but were approximately 1–9 percentage points less likely to succeed than their Bangladeshi (+1.3pp), Black African (+1.9pp), Indian (+7.2pp) and Chinese (+8.7pp) peers.

The principles of QuantCrit here remind us that data generated by government bodies are likely to embody dominant (racialised and racist) assumptions i.e. the numbers presented in official publications, such as attainment data, are not likely to speak for minority ethnic interest, but for White interests, and for the preservation of the racial status quo (Garcia, López, and Vélez 2018; Gillborn, Warmington, and Demack 2018). Therefore, a central question to be addressed is: whose interests are being served (and whose are silenced) by the dominant presentation of official attainment statistics?

Despite sensationalist headlines to the contrary, as a group, roughly nine-in-ten White children continue to outperform their historically underperforming Black Caribbean peers. By focusing on the achievement data of the one-in-ten (FSM) to the exclusion of the nine-in-ten (non-FSM), the government and media encourage some 60 per cent of Britons who identify as ‘White-British’ and ‘working class’ (NatCen 2015) to believe that every minority ethnic group outperforms their children. Equally – in utilising the performance data for the one-in-ten White students – the reader of official statistics is directed to disregard the enduring underperformance of Black Caribbean children (erasing the advantaged position of roughly nine-in-ten White students).

To be clear, I am not challenging the view that White British children claiming FSMs are the lowest performing of the main ethnic groups in state schools¹⁶; my argument is that selective use of official attainment statistics, emboldened by the erroneous label ‘White working class’ to refer to just one-in-ten, creates an unfounded perception of a mass of White casualties in Britain’s schools.

The improved GCSE results of Black children, in particular, gave rise to news headlines to include ‘Must do better? Black pupils did, with best improvement in exams’ (Independent [Garner 2014]), and ‘Who is top of the class? Black children achieve biggest rise in test and exam results of any ethnic group’ (Daily Mail [Evans, June 2014]). Yet, compared to nine-in-ten White students, Black Caribbean students continue to underachieve as demonstrated in Table 3. For any critical scholar, with specific interests in the racialised experiences of Black children in schools, the shifting of focus to the underperformance of the ‘White working class’ child, to the exclusion of other groups, serves only to further obscure the way in which education policies and practices in the UK continue to produce racialised outcomes.

‘Gold standard’ qualifications and the white working class

In this section the analysis turns to the kinds of qualification that make up the headline achievement statistics and, in particular, examines the change in apparent group success rates that occur when a different government benchmark – a so-called ‘gold standard’ – is applied. In the competition for places in employment or higher education in the UK, the qualification prerequisite often stipulates a ‘minimum of a grade C in English and maths GCSE’.¹⁷ Achieving a designated ‘equivalent qualification’ in school – an alternative to the English and Maths GCSE – is widely perceived as an inferior qualification, easier to achieve, or in some cases not equivalent at all (even illegitimate). For example, Bill Watkin, Chief Executive of the Sixth Form Colleges Association¹⁸ has suggested that for employers, a traditional GCSE ‘will give a job applicant an edge’, and for many higher and further education providers a traditional ‘GCSE is still the currency of choice’ (Watkin 2016). Given the greater societal and market value placed on the so-called ‘rigorous’, ‘gold standard’ ‘traditional GCSE’ qualifications (the very language utilised by the Department of Education to describe the GCSE [DfE 2015]), an important question arises in relation to the selective use of data pertaining to an attainment benchmark that does not necessarily include any traditional GCSEs.

Schools in England are allowed to include vocational and so-called ‘functional’ qualifications as ‘equivalent to GCSE’ within their official performance data. For example, schools can report successfully completed ‘BTECs¹⁹’ and ‘GNVQs²⁰’ (in vocational subjects such as Art and Design, Media, Health and Social Care, Performing Arts, Sport, and Travel and Tourism), and ‘Functional Skills’ (in English, maths and ICT), as equivalent to GCSE. Thus in theory, a student could have satisfied the benchmark ‘5+GCSE A*-C’ as outlined in Tables 2 and 3, with five ‘equivalent’ qualifications and no ‘traditional,’ ‘gold standard’ GCSEs; which raises significant questions about the type and relative value of the qualifications obtained by the students included in Tables 2 and 3.

Before examining the data it is worth noting that access to the necessary material is restricted. Despite the government’s claim to be ‘the most transparent government in the world’ (Conservative Party 2015, 49), in the data published by the Department for Education ‘Statistical First Release’²¹, it is not possible to isolate the traditional GCSE in English and math from their ‘equivalents’ within the benchmark ‘5+GCSE A*-C’ in a form that desegregates by ethnic origin, gender and FSM. ‘Special permissions’ are necessary to access the more detailed material from the National Pupil Database (NPD), which required the completion of a complex application form, institutional backing, and formal research training.

As identified in the previous sections the plight of White working class boys, in particular, has been emphasized by the government and media. This group, as identified in Tables 2 and 3, were the lowest, or second lowest, performing group in the attainment benchmark of achieving ‘5+GCSEs A*-C’ (i.e. 5+GCSEs or equivalents graded A*-C [Table 3]) and they continue to feature in striking headlines:

Education Select Committee Member and Member of Parliament, Wragg: ‘White working-class boys: The vulnerable group in UK schools’

(PoliticsFirst [Wragg 2017])

To examine the impact of ‘equivalent’ qualifications, the first part of Table 4 re-produces data from Table 3 for the ‘basic’ benchmark ‘5+GCSEs A*-C’ (including equivalents), with the addition of NPD data for the ‘gold standard’ benchmark of ‘5+GCSEs A*-C including English & Math GCSE’ (excluding ‘equivalent’ qualifications).²²

Table 4. Change in success rate when ‘equivalent qualifications’ are removed.

		2010-2011				Decrease in Success Rate between Benchmarks (% points)		2016-2017
		Basic Benchmark [i]		Gold Standard Benchmark [ii]				‘Strong’ Benchmark [iii]
		[a] FSM	[b] N-FSM	[c] FSM	[d] N-FSM	FSM [a - c]	N-FSM [b - d]	FSM	n-FSM
	White British	54.8	79.6	18.2	51.5	-36.6	-28.1	14.8	42.3
	Indian	75.2	88.8	43.4	66.3	-31.8	-22.5	41.3	60.2
	Pakistani	71.0	80.1	28.9	44.3	-26.7	-35.8	27.4	38.7
Male	Bangladeshi	76.8	81.5	43.8	49.9	-26.9	-31.6	40.7	49.6
	Black Caribbean	64.6	74.5	21.9	36.0	-42.7	-38.5	14.3	27.1
	Black African	73.2	82.4	32.7	48.8	-40.5	-33.6	30.9	41.8
	Chinese	81.9	91.3	59.0	71.0	-22.9	-20.3	62.3	68.2
	White British	64.6	86.3	23.3	59.6	-41.3	-26.7	19.1	48.7
	Indian	85.9	93.5	55.4	76.6	-30.5	-16.9	45.3	66.8
	Pakistani	78.6	86.5	38.9	55.2	-39.7	-31.3	31.0	43.4
Female	Bangladeshi	84.5	87.6	51.7	59.9	-32.8	-28.0	43.1	52.1
	Black Caribbean	76.5	84.5	34.1	52.0	-42.4	-32.5	25.0	35.4
	Black African	78.3	88.2	44.8	63.6	-33.5	-24.6	38.3	50.2
	Chinese	94.9	95.0	73.8	80.7	21.1	-14.3	74.2	76.7

[i] ‘5+ GCSEs graded A* - C’

[ii] ‘5+ GCSEs graded A* -C inc. English and Maths GCSE’ – GCSE only excluding equivalent ‘equivalent qualifications’ in English and Maths

[iii] ‘Percentage of pupils achieving grade 5 or above in English and Maths GCSE’

Sources: National Pupil Database (England Maintained Schools) KS4 data (2010-2011), National and Local Authority Tables SFR03/2012 (Table 2a), and SFR01/2018 (Table 2a)

As seen in the first column, when the focus is on the performance of the approximately one-in-ten White males claiming FSMs, and the ‘basic benchmark’ is applied (5 or more GCSEs graded A* to C [any subject including GCSE and equivalent qualifications]), it appears that all other major groups outperform White British males [column a in Table 4]. However, when the focus is on the performance of the nine-in-ten White males not claiming FSMs, and the ‘gold standard’ benchmark is applied (5 or more GCSEs graded A* to C including English and Maths [GCSE qualifications only excluding equivalent qualifications]), as a group, non-FSM White males outperform their Pakistani, Bangladeshi, Black African and Black Caribbean peers [column d in Table 4]; they are, in fact, the 3^rd highest performing of the principal ethnic groups in the UK, behind Chinese and Indian students (who collectively represent 2.7 per cent of the total population of male KS4 students in 2011).²³

For female students, when the focus is on the performance of the approximately one-in-ten White females claiming FSMs, and the ‘basic benchmark’ is applied, all other major groups are seen to outperform White British females. However, when the focus is on the performance of the nine-in-ten White females not claiming FSMs, and the ‘gold standard’ benchmark is applied, as a group, non-FSM White females continue to outperform their Pakistani and Black Caribbean peers, and near equal the performance of their Bangladeshi peers.

In addition, the analysis reveals a disturbing picture when we examine the change in success rates between the two benchmarks (FSM [a minus c] and N-FSM [b minus d]). Of all reported groups in Table 4, Black Caribbean students, both male and female, are the most likely to be adversely affected in terms of the percentage point decrease in success rate that is linked to a change from a ‘basic’ (any subject, including equivalents) to a ‘gold standard’ benchmark (including English and Maths GCSEs, excluding equivalent qualifications) – regardless of their FSM status. Compared to ‘nine-in-ten’ White students (non-FSM), Pakistani, Bangladeshi, and Black Caribbean students of both genders, and Black African males, are less likely to achieve success through the traditional gold-standard, higher value government benchmark – a deeply concerning finding, that has not been recognized before.

Of the reported principal male groups, Black African and Black Caribbean males were negatively affected by the change in benchmark – irrespective of their FSM status. For male students not claiming FSMs – Pakistani, Bangladeshi, Black Caribbean and Black African students were more adversely affected by the change in benchmark than their White peers, seeing significant decreases in success rates of between 31.6 and 38.5 percentage points. Of the reported principal female groups, Black Caribbean students were most detrimentally affected by the change in benchmarks – again, regardless of their FSM status. Compared to female students not claiming FSMs, Pakistani, Bangladeshi and Black Caribbean students were most adversely affected, with a decrease in success rate of between 28.0 and 32.5 percentage points. As seen in the final column of Table 4, the racialised pattern of higher-status achievement identified in 2011 (gold benchmark) remains entrenched in 2017 against a newly introduced 'strong' benchmark ('achieving a level 5 or more in GCSE English and Maths') (Conservative government, see Busby, 2017).

In this way, QuantCrit prompts researchers to explore data for otherwise hidden or unrecognised areas of inequity i.e. raced inequities that may currently go unrecognised or be viewed merely as ‘business-as-usual’ (Delgado & Stafancic, 2000, xvi). Thus, the racialised pattern of lower-status and equivalent qualifications as noted in Table 4, indicate that there is evidence to support the notion that schools are disproportionately bolstering the performance data of certain minority ethnic groups at KS4 through attainment in qualifications that are officially ‘equivalent’ but lack the societal and market status accorded to traditional GCSE credentials in the labour and educational marketplaces. This difference goes unrecognized in the headline statistics, published on government websites and trumpeted in the media, which simply focus on the apparently greater overall attainment of minoritized students without noting the different value of the qualifications.

Conclusion

Poor white boys are the new oppressed (Phillips, 2017)

Nonpoor Whites think that there is no need to talk about poor Whites unless Whiteness is the main topic of discussion…. nonpoor Whites’ evocation of poor Whites through the phrase ‘What about poor White people?’ warrants further examination. (Allen 2006, 209)

In this paper I have applied the principles of QuantCrit (Garcia, López, and Vélez 2018; Gillborn, Warmington, and Demack 2018) to explore the hidden dynamics of race, gender and poverty intersections that lie behind the easy and misleading headlines about ‘White working class boys’. I illustrated how the British government’s deployment of the knowingly inaccurate label ‘White working class’ (as applied to White children claiming FSMs), provides a dangerous veneer of White-ethnic disadvantage that fuels a sense of siege. The analysis also revealed a disturbing pattern of equivalency through which,Pakistani, Bangladeshi, and Black Caribbean students of both sexes, and Black African males, are less likely to achieve success through the traditional gold-standard, higher value government benchmark (5+GCSEs A*-C inc. English and math [GCSE only exc. equivalents]), compared to nine-in ten White British peers (non-FSM). In line with the principles of QuantCrit, therefore, this paper demonstrates that statistics are not ‘value free’ nor politically ‘neutral’: official statistics, such as the KS4 data explored in this paper, are at the very heart of an institutional process through which race and racism are produced and legitimised in society. When the numbers are subjected to race-critical scrutiny it becomes clear that headlines about ‘white working class’ failure dramatically misrepresent the scale of the issue (by appealing to more than half the population on the basis of data derived from one-in-ten White students) and that an area of minority disadvantage (that Black Caribbean students are the most likely principal ethnic group be entered for lower-status or ‘equivalent’ qualifications – regardless of FSM status) has gone entirely unrecognized.

The significance of the analyses set out in this paper relates to the wider politics of race equity generally and, in particular, the raced dynamics of state education. My focus, on the definition and (mis)representation of class identities and levels of achievement, does not arise from an isolated concern with technical quantitative questions for their own sake; in contrast, and drawing on the principles of QuantCrit, the analysis seeks to understand how racialised assumptions and inequities are (re)made in the construction and reporting of English educational statistics.

When the Conservative government published the inaugural ‘Race Disparity Audit’ (Office 2017) it claimed that by releasing a slew of statistical data it was striking a blow for race equality. The Prime Minister, Theresa May, was quoted stating that ‘The findings from the Race Disparity Audit present us with a real opportunity to make transformative change in tackling persistent race inequality’ (May 2017). Far from focusing attention on minority ethnic students, however, as the headline from one of the country’s biggest selling national newspapers (Daily Mail, see Phillips 2017 above) demonstrates, the press were quick to use the data to point to the apparent ‘oppression’ of White students, especially ‘poor white boys’.

The manipulation and selective use of achievement data, as explored in this paper, helps to generate and sustain a toxic political climate in which the White working class – or to be precise those 60 per cent who believe themselves to be working class (NatCen 2015) – come to wrongly understand their children as race victims in the nation’s schools; it is a perverted sense of ‘reverse’ or ‘anti-White’ racism through which the ordinary White citizen is reimagined as a race victim. As Ricky Lee Allan has noted, in relation to popular tropes of White disadvantage in the US, the concerted cry – ‘what about the White working class’ – powerfully establishes the group as distinctly and racially disadvantaged in the nation’s schools. By robustly bolstering the image of a disadvantaged White collective, wealthy and empowered Whites can be seen to fight for, and make explicit their commitment to, their disadvantaged White siblings (Allen 2006). In this way, the analysis above could be interpreted as evidence that the disproportionately White ²⁴ privately or selectively-educated government, with the support of the equally White-British media, can be seen making an empathetic commitment to the White working class and their children as a racialized group, e.g. Theresa May’s explicit commitment to a Race Audit through which the government will show ‘disadvantages suffered by White working class’ in addition to ‘ethnic minorities’ (May 2016). Clearly, as Allen (2006) argues, the complex relationship between the arguably de-racialised ‘wealthier-White’ group and the highly racialised ‘White working-class’ group is a dynamic that needs urgent and continued critical investigation.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Notes on contributors

Claire E. Crawford

Claire E. Crawford is a BRIDGE Research Fellow based at the Centre for Research in Race & Education (CRRE), School of Education, University of Birmingham, UK. She is also an affiliate member of the Office of Community College Research and Leadership (OCCRL) at the University of Illinois at Urbana-Champaign, USA. Claire's current research focuses on trans-Atlantic formations of race inequity in education and, in particular, the application of quantitative methods within critical race theory.

Notes

1. Of note, British journalism is approximately 94% White (Guardian [Williams, 2016]).

2. Approximately 8 per cent of British Members of Parliament (House of Commons) are from an ethnic minority background (House of Commons [Briefing paper SN01156], 2017).

3. The Conservative-Liberal Democrat coalition was the first full coalition government in Britain since 1945. The Conservative party is a center-right political party and the Liberal Democrats a liberal-center/center-right party. Prior to the election of the coalition government, Labour, a loosely center-left party, had been in power since 1997.

4. To include – instituting ‘bold reforms to drive up standards in schools’; restoration of ‘the integrity of our qualifications’; and, the ‘introduction of “a new, more ambitious national curriculum”’ (DfE, 2016, 3).

5. Students attending private schools are not counted in the majority of official education statistics in England.

6. Data is collected every term via schools and includes information for each pupil such as their name, address, date of birth, gender, ethnicity, whether they have been identified as having special education needs, whether they are looked after the by local authority (or have ever been), and whether they are eligible for free school meals. Data collected in the School Census is utilised and strictly controlled by the Department for Education (DfE).

7. Crime justice and the law; Culture and community; Health; Housing; Work, pay and benefits.

8. A CRT analysis, therefore, assumes a fundamentally different approach; whereas a traditional statistical approach might be to run a regression analysis to see whether any ‘race’ effect is left over, a critical race theorist assumes that race/racism will be an important aspect of the processes and explores different ways of understanding this; they do not assume that any single statistical approach will automatically or adequately reveal its full complexity or scale.

9. The Office for Fair Access is the independent regulator of fair access to higher education in England.

10. Which may not necessarily include all those entitled to apply for, or claim, FSMs. For example for personal/circumstantial reasons, those students entitled to FSMs, may not be in receipt of them.

11. Table 1 includes data on the largest ethnic groups in England, with the addition of Chinese students. Chinese students, despite their small size, are treated as a ‘major ethnic group’ in UK government publications. For example, in the annual report ‘Schools, pupils and their characteristics’ (DfE, 2017), performance data pertaining to Chinese students is not subsumed under an ‘Asian’ collective; unlike data for the larger Indian, Pakisatani and Bangladeshi groups.

12. GCSE examinations (General Certificate of Secondary Education) are taken by most students at the end of compulsory aged school (age 16) in England, Wales and Northern Ireland. A GCSE is awarded in a specified subject, and students generally take a number of subjects (typically 8–10) over the final two years of compulsory aged schooling.

13. Of note, data on ‘White – Traveller of Irish Heritage’ and ‘White – Gypsy/Roma’ are reported as separate to ‘White – White British’ by the Department for Education.

14. White males – achievement gap is reduced from 10–27 pp (FSM), to 2–12 pp (non-FSM).

15. White females – gap reduced from 12–30 pp (FSM), to 1–9 pp (non-FSM).

16. Of all reported groups in the UK ‘White – Traveller of Irish Heritage’ and ‘White – Gypsy/Roma’ children, whether FSM or N-FSM are consistently the lowest performing groups [SFR03/2012] – e.g. in 2011 White Gypsy/Roma groups claiming FSMs underperformed their White British peers claiming FSMs by 16.5–20.5 percentage points.

17. For example, almost without exception, initial teacher training courses in the UK require English and Math GCSEs or approved ‘iGCSEs’ (International GCSEs).

18. In the UK, ‘sixth form’ is a term to denote the period of school/college study between the end of compulsory education (age 16) and entering higher education (age 18).

19. BTEC – Business and Technology Education Council.

20. GNVQ – General National Vocational Qualification.

21. I.e. the report ‘GCSE and Equivalent Attainment by Pupil Characteristics in England (2010/11)’ [DfE 2011 SFR03/2012].

22. NB – As outlined in the sources and statistics section of this paper, SFR data is derived from the NPD, and as such, both data sets can be reliably compared within the same table.

23. Compared to White British students who represent 78.2 per cent of the total population (Source: National and Local Authority Tables SFR03/2012 [Table 2(a)]).

24. Approximately 8 per cent of British Members of Parliament (House of Commons) are from an ethnic minority background (House of Commons [Briefing paper SN01156], 2017); 32 per cent of MPs in the House of Commons were privately educated, while 19 per cent attended selective entry grammar schools (BBC [Burns, 2015]); ‘British journalism is 94% white’ and only ‘0.2% Black’ (Guardian [Williams, 2016]).