British Educational Research Journal

Introduction

There is a long‐standing skills gap in the supply of graduates with much‐sought‐after expertise in STEM (Science, Technology, Engineering and Mathematics) subjects, causing concern for how economies will cope with our increasing dependence on technology in everyday life (Winterbotham, 2014). A rich literature has emerged, with policy‐makers, academics and stakeholders in industry working to further understand the full extent of the problem. The Social Market Foundation has identified an existing shortage of up to 40,000 workers with STEM skills, and considering trends in industry it is predicted that this will increase significantly if steps are not taken to close the gap (Broughton, 2013). A particular problem is that socio‐economic background, gender and ethnicity are all associated with the study of STEM subjects (CaSE, 2012; Equality Challenge Unit, 2014).

The economic case for increased participation and diversity in STEM fields is clear, but there are also substantial benefits to be had for individuals. For example, those who study STEM subjects at degree level and General Certificate of Education (GCE) Advanced Level (A level) typically earn higher salaries later in life (Dolton & Vignoles, 2002; Greenwood et al., 2011). Despite this, the problem of low uptake seems a particularly large concern in the UK, which has one of the lowest shares of 15‐year‐olds aspiring to pursue STEM careers of OECD countries (OECD, 2012). In the interests of the promotion of social mobility and equality of opportunity, it is important that individual benefits are not restricted by a student's social background, gender or ethnicity. Recent policy changes have led to an increase in post‐compulsory mathematics qualifications available (Department for Education, 2014), which may contribute to increased basic skills in maths, however, they may not necessarily lead to an increase in participation at degree level. It is therefore important to understand which students do not study STEM subjects, and why particular groups have lower participation.

Prior research in the area has considered reasons for decreased participation in STEM subjects for all students, often with particular focus on gender disparities. Reasons put forward for lack of engagement include students’ values, perceptions of the importance and relevance of STEM, shortages of maths and science teachers, perceptions that STEM subjects are more difficult or ‘boring’ compared with other subjects (Wynarczyk & Hale, 2008), and teaching methods and styles (e.g. Gilbert, 2006; Pampaka et al., 2012a,b). In response to decreasing participation, a large research initiative—the Targeted Initiative on Science and Maths Education (TISME)—was set up in the UK. Key findings from five large‐scale projects included that the perception of ability and knowledge of usefulness of STEM appeared to drive issues with uptake, rather than interest in or enjoyment of science (TISME, 2013). Furthermore, science capital in families was an important driver of choice; students whose parents were engaged with STEM or worked in STEM careers were more likely to study STEM further (Archer et al., 2012). There is less research, however, on how these mechanisms relate specifically to student characteristics, especially in respect to students’ background and ethnicity. An important prerequisite to understanding exactly which mechanisms lead to decreased engagement amongst particular groups is to fully understand which student characteristics are associated with choice, and how.

Research questions

• What is the relationship between students’ family background, gender and ethnicity with choice of STEM study at A level and university?
• Can disparities in uptake be explained by students’ prior academic attainment?
• Do students’ characteristics interact in determining choices?

This paper proceeds as follows. The first section describes, under ‘methodology’, the data used for analysis, relevant variables and analytical strategy. The second section quantifies the proportions of students studying STEM at A level by students’ gender, ethnicity and family background, and interactions between these characteristics. The third section considers HE subject choices. The fourth section concludes with a discussion of results and possible implications for policy and research.

Methodology

Analytical strategy

I first present raw descriptive statistics for students’ choice of STEM A level, and of STEM and SLB subjects in HE, comparing proportions of students choosing each group of subjects by ethnicity and family background across genders. To understand which characteristics are most important in explaining students’ subject choices, and how students’ family background, gender and ethnicity interact in determining choice, I use logistic regression models. Regression methods identify the unique associations of each predictor variable with students’ choices, thus allowing identification of which student characteristics explain the largest proportion of variance in choice, whilst other predictors are held constant.

Models are built up in three stages. Model 1 predicts students’ subject choices based on their characteristics only. For A‐level choices this is choice of at least one STEM A level compared with no STEM A levels. For degree subject choice, this is choice of STEM, SLB or arts and humanities subjects. Model 2 controls for prior attainment across subjects, and Model 3 includes interaction terms. For degree choices an additional fourth model is run, which also includes indicators for whether students studied STEM subjects at A level, to assess whether associations between student characteristics and degree choices are significant over and above their relationship with A‐level choices.

The motivation for including all characteristics in the first model, rather than looking at raw proportions, is that student characteristics are strongly correlated. For example, students’ SEB and ethnicity are strongly intertwined; the Labour Force Survey 2004 and the Pupil Leave School Census 2002 showed strikingly large differences in proportions of students claiming FSM (Bhattacharyya et al., 2003) or in relative income poverty (Kenway & Palmer, 2007). For this reason, it is likely that models not taking account of both student characteristics will under or over‐estimate the diversity of uptake of STEM subjects. In the samples used for analysis, there are large differences in students’ family background by their ethnicity. Table 3 outlines the proportions of students claiming FSM by ethnicity, which broadly reflect the proportions reported by Bhattacharyya et al. (2003). Students’ attainment is also related to characteristics; students from lower SEBs especially are more likely to have lower levels of prior attainment, so it would be expected that some of the differences in subject choice (especially choice of STEM subjects, which are considered ‘harder’ than other subjects) would reduce when accounting for attainment.

Table 3. Differences in proportions of students claiming FSM by ethnicitya

Ethnicity	Unweighted count	Proportion claiming FSM
White British	2589	3.0%
Mixed	183	10.9%
Indian	478	6.6%
Pakistani	257	30.8%
Bangladeshi	232	56.4%
Black Caribbean	110	13.6%
Black African	154	28.3%
Other	132	20.7%

• ^a Taken from the sample attending university; results were similar for the A‐level sample.

How do student characteristics interact in determining A‐level subject choice?

Students typically study between three and four A levels, and given university entrance requirements it is unlikely that students who do not study at least one STEM A level will study a STEM subject at university. Proportions of female and male students from each ethnic group studying STEM A levels in the Next Steps sample are shown in Figure 1. As predicted, male students are more likely to study at least one STEM A level. Overall, Indian, Pakistani and ‘other ethnicity’ students are more likely to study STEM A levels than students from other ethnicities. White, Black African and Black Caribbean students have particularly low levels of relative uptake. There appear to be gender differences in uptake across the majority of ethnicities, with the exception of mixed ethnicity and Black Caribbean students, where there are no gender differences. Female students of mixed ethnicity and Black Caribbean ethnicity are more likely to study STEM A levels than white female students, whereas Black Caribbean male students are less likely to study STEM than white male students. For Bangladeshi students there is a particularly large gender disparity in proportions of students studying STEM, with just over 20% of young Bangladeshi women choosing STEM subjects at A level compared with over 50% of young Bangladeshi men.

Table 4 illustrates the relationship between students’ family background, gender and subject choice. Male students taking at least one STEM A level are more likely to be in higher income bands, and all students choosing STEM A levels are more likely to have parents with higher educational achievements and in higher occupational classes than students who were not studying any STEM subjects. They are also more likely to be attending independent schools, and to be in the highest SEP group.

Table 4. Family background characteristics of female (male) students completing at least one STEM A levela

Subject	Take at least 1 STEM subject	No STEM subject
Median income band	£28,600–£31,200 (£31,200–£33,800)	£28,600–£31,200 (£28,600–£31,200)
Mother has degree or higher	20.2% (23.4%)	14.0% (16.3%)
Father has degree or higher	25.4% (26.3%)	13.4% (17.1%)
Household has service class occupation	58.1% (57.7%)	50.0% (52.1%)
Independently educated	19% (15.6%)	11% (13.9%)
Highest SEP	36.7% (40.7%)	27.7% (29.3%)

• ^a Incomes are at 2003 prices, measured in Wave 1 data collection. ‘Service class’ includes parents in higher and lower managerial and professional occupations; parent with ‘at least some HE’ includes parents with some HE and those with a degree qualification or higher.

Regression models of A‐level subject choices

Logistic regression results of the relationship between students’ characteristics and subject choices are shown in Table 5. The first model includes students’ ethnicity, family background indicators and school type. The second model additionally includes students’ prior academic attainment and the third model includes interaction terms. Figure 2 illustrates differences in students’ odds of choosing at least one STEM A level by ethnicity, with the blue dots illustrating odds before conditioning on attainment (taken from Model 1) and the purple dots illustrating odds after conditioning on attainment (taken from Model 2). Differences in choices by ethnicity broadly reflect raw associations, however, Figure 2 shows that with the addition of prior attainment to the regressions, differences in uptake increase substantially. This suggests that the full extent of disparities in choice by ethnicity is supressed by attainment differences, which influence choices in the opposite direction.

One possible reason why BME students may be more likely to choose higher‐return STEM subjects could be related to differences in parental and student attitudes and behaviours; BME groups generally have more favourable scores on these characteristics when considering outcomes (Strand, 2011). Whilst Strand found that an increase in these attitudes and behaviours does not lead to proportionately higher academic attainment, they could influence student choices.

Students’ social class and parents’ education are both uniquely related to choices. Students whose parents work in managerial occupations are more likely to study STEM than students with parents in working‐class occupations. The relationship is, however, fully explained by prior attainment. Students from higher social classes are more likely to achieve higher grades, which in turn predicts participation in STEM A levels. Parents’ education levels have differing associations with STEM study, which persist when conditioning on attainment. Students whose mothers have a degree are less likely to study STEM A levels, whilst students whose fathers have a degree are more likely to study STEM A levels. Figure 3 illustrates this relationship between students’ family background and choice of STEM A levels with all student characteristics, attainment measures and interaction terms controlled, showing how the association between both parents’ education and choices persists, whilst other background characteristics are no longer significantly associated with choices.

Compared with other family background characteristics, parental income and whether students attended independent school are not associated with participation in STEM. This suggests that relationships between type of school and participation are driven by students’ parents’ education and social class, rather than differences in schooling.

Overall, students’ prior attainment is positively associated with choice, with the exception of KS2 English attainment. This is in line with research by Wang et al. (2013), and suggests that students who do well in English are choosing to pursue other subjects. It is noted that due to the issue of multicollinearity, care should be taken when interpreting the odds ratios on attainment scores; scores are likely to be highly correlated and therefore exact values would change considerably with the addition or subtraction of indicators in the model. As it stands, we can only confidently ascertain direction of association and the cumulative effect of attainment indicators on other associations.³

Overall, there are few interactions between student characteristics and A‐level choices, the only exception being that more advantaged Bangladeshi students are less likely to pursue STEM subjects at A level.

How do student characteristics interact in determining HE subject choice?

There are well‐established differences in choice by students’ gender; male students are more likely to study STEM subjects at university, whilst female students are more likely to study arts and humanities. In terms of ethnicity, HESA data covering students across the UK also reveals that overall, students from BME backgrounds are more likely to study STEM and SLB subjects and less likely to study arts and humanities subjects, although there is large heterogeneity between ethnic groups and subjects (Equality Challenge Unit, 2014). The Next Steps data also indicates that there are large differences in participation by students’ gender and ethnicity, as shown in Figure 4. White students are least likely to study high‐return SLB subjects, whilst Asian students are most highly represented, and this increase in uptake is mirrored by very low uptake of arts and humanities subjects. Black Caribbean and Black African students stand out as being particularly under‐represented in STEM.

Table 6 illustrates the raw relationships between family background, gender and subject choice. There are small differences in average income of students in each subject group. Female students studying SLB subjects have the lowest median family incomes, whereas young men studying either SLB or arts and humanities subjects have the lowest family incomes. Students studying STEM and arts and humanities subjects are most likely to have parents with a degree or higher, and in service‐class occupations, compared with students studying SLB subjects. In contrast, SLB subjects appear to attract the highest proportions of independently educated students. In considering students’ SEP, SLB subjects stand out as having particularly low uptake amongst the most advantaged female students, whilst for male students, differences between groups are small.

Regression models of HE subject choices

Table 7 presents results from multinomial logistic regressions of the relationship between subject studied and students’ characteristics. Like A‐level choices, regression models were built up in stages, with the first model including only student characteristics and school type, the second model conditioning on attainment and the third model including interaction terms. A fourth model is run, including indicators for whether students studied STEM at A level.

Differences in choice by ethnicity are strikingly large. The first model shows that, even after accounting for family background, students from BME backgrounds are less likely to study arts and humanities subjects, and more likely to study SLB subjects, than STEM subjects. Black Caribbean students and students of mixed ethnicity, however, are most similar to white students in their choices, and are no more likely to study STEM (see Figure 5).

In line with raw associations and prior research, differences in uptake of STEM and other subjects are observed by students’ family background (Gorard et al., 2008; The Royal Society, 2008). Whilst social class and family income are not significantly associated with choices, parental education (particularly mothers’ highest qualification) is. Students whose mothers have a degree are more likely to study arts and humanities than STEM subjects, even when prior attainment differences are taken into account. Students whose fathers have a degree are more likely to study STEM than SLB subjects, however, this relationship is fully explained by attainment differences (see Figure 6).

It might be expected, given that STEM and SLB subjects offer higher financial returns, that family income would be associated with choices, for example students from higher‐income families may be more concerned with financial returns after study (e.g. Davies et al., 2013). Alternatively, students from lower‐income families may be more inclined to avoid more risky subjects when considering outcomes (e.g. Breen & Goldthorpe, 1997) and choose ‘easier’ subjects. Despite this, and raw statistics indicate otherwise, when taking account of other student characteristics, family income is not related to subject studied. In terms of schooling, there is an indication that independently educated students, all else held equal, are more likely to study high‐return SLB subjects over STEM subjects.

Interactions are observed between students’ social background and gender, and between ethnicity and gender. As students’ SEP increases, young women are less likely to choose SLB subjects and more likely to choose to study STEM subjects. This suggests that young women from more deprived backgrounds may be particularly vulnerable to factors driving students away from STEM. Black African female students, however, are much more likely to choose STEM over arts and humanities.

Model 4 shows that when including indicators for whether students studied one STEM A level or two or more STEM A levels, results are largely similar. Students studying STEM at A level were considerably more likely to study STEM subjects at degree over both arts and humanities, and SLB subjects. Taking account of A‐level choices did affect some ethnic differences in participation, for example Indian students who studied STEM at A level were not significantly more likely to choose STEM over arts and humanities than white students who also studied STEM A levels. In contrast, when accounting for A‐level choices, Pakistani and (to a lesser extent) Bangladeshi students were more likely to choose SLB over STEM compared with white students. The social background disparities persisted and increased somewhat, with students whose mothers had a degree remaining more likely to study arts and humanities than students with lower levels of education. The interactions between gender and SEP, and between Black African ethnicity and gender in determining uptake, also persisted.

Discussion

This paper aimed to describe disparities in students’ subject choices by their family background, ethnicity and gender, and to unpick the more complex relationships between these characteristics. I focused specifically on uptake of STEM subjects at A level and HE because these subjects have high levels of disparity in uptake across student characteristics, as well as numerous benefits of study to both individuals and society. For HE choices this was compared with uptake of two other groups of subjects: SLB subjects, which offer higher returns on graduation to individuals, and arts and humanities subjects. Although research into educational achievement disparities has started to look at how student characteristics interact to produce outcomes, rather than simply how they additively lead to deficit in attainment, studies of students’ subject choices have not yet considered more complex models. The study addressed this by looking at whether family background could explain disparities in uptake by students’ ethnicity, and whether patterns of choice differed for male and female students, or across socio‐economic groups.

The findings complement a growing literature profiling disparities in uptake of STEM subjects (e.g. Gorard et al., 2008; Boaler et al., 2011; Botcherby & Buckner, 2012). In the Next Steps sample, students of almost all minority ethnic groups were more likely to study STEM and SLB subjects given family background, and this association increased when taking account of their prior attainment. Although generally there were similar patterns of uptake by students’ ethnicity across genders, the interaction between Black African ethnicity and gender suggests that Black African women are more likely to study STEM than arts and humanities. This is in contrast to raw data suggesting that Black African and Caribbean students are less likely to study STEM subjects when family background is not accounted for (Boaler et al., 2011). It is possible that the underlying reasons for these differences, whether driven by cultural differences or biases (institutional or individual), are affecting young women and men differently. The findings offer additional evidence of the relative lack of ethnic diversity in arts and humanities subjects, where white students are disproportionately represented compared with all other ethnic groups. In terms of theories of relative risk aversion, given that there appear to be some additional barriers within HE and upon graduation for BME students, they may be making very rational choices to study subjects which have more secure prospects and higher financial returns. For example, research figures show that in the UK, minority ethnicity students are less likely to receive high degree classifications and are more likely to be unemployed after graduation (Runnymede Trust, 2014).

This paper adds to the literature by considering a more comprehensive range of indicators for students’ family background, including income, parents’ education, occupational status and type of school attended. It appears that parental education, but not social class or financial resources, influence students’ choices. Students studying STEM A levels are more likely to have fathers with a degree, and less likely to have mothers with a degree. At degree level, students whose mothers have a degree are most likely to study arts and humanities. It is possible that this relationship is related to the subject parents are educated in, and relative ‘science capital’ in the family (Archer et al., 2012). As mothers are more likely to have non‐science degrees than fathers, they may influence their children to study other subjects. Because the LSYPE data does not include subjects studied by parents, this isn't something that could be explored further in the current study.

The interaction between students’ family background and gender suggests that young women from more advantaged backgrounds are more likely to choose STEM subjects, whilst those from relatively deprived backgrounds are more likely to study SLB subjects, which—although they offer high individual returns—are not considered ‘difficult’ compared with STEM subjects. In accordance with the theory of relative risk aversion, more advantaged female students may be choosing more ‘risky’ high‐return subjects compared with their less advantaged peers.

As with ethnicity, there isn't sufficient evidence that young women have an innate difference in ability to young men, and much research has profiled the many institutional biases that may push young women away from STEM subjects. STEM subjects are stereotypically seen as more ‘masculine’ domains, and in school, girls with the same academic attainment as boys are less likely to be rated as high achieving in maths by teachers (Campbell, 2015) and less likely to receive positive reinforcement from teachers (Mujtaba & Reiss, 2012), which may affect self‐efficacy beliefs. Although the reasons are unclear, girls are less likely to be interested in science, and more likely to be interested in people, than boys (Collings & Smithers, 1984). What sets this work apart is the finding that disparities are not constant, but differ by students’ family circumstances. Given the institutional factors at play throughout students’ lives, it may be that the processes involved in overcoming stereotypes are also associated with students’ background. Students from lower SEPs may be more likely to feel constrained by their gender and to feel that they have less control over their future, which may in turn be related to uptake (e.g. Mau et al., 1995).

It could also be that students’ family background is related to parents’ attitudes and behaviours, which mediate the relationships observed. If mothers with higher education levels have more egalitarian views of gender roles (Crompton & Lyonette, 2005), these views may be transmitted to their children (Kulik, 2002; Antill et al., 2003) and thus directly or indirectly influence young women's interests and values when choosing courses. Future research could focus specifically on whether student and parental attitudes and behaviours mediate the relationship between students’ characteristics, SEP and subject choices.

There are various strengths to the analysis presented. Based on observable characteristics, the LSYPE is generally representative of the population, and weights are applied where this is not the case. This is a recent sample, and students’ subject choices in 2008–2010 are analysed. Furthermore, I have included a rich set of student family background characteristics to draw evidence from, and the longitudinal nature of the dataset allows me to assess whether student circumstances at age 13–14 can predict later subject choices. Despite these strengths, there remain some limitations to the study. Although weights have been applied to ensure the data are representative, these could only be modelled on observed characteristics, and it is possible that there are some unobserved characteristics related to both non‐participation and subject choice. In addition, the majority of indicators (with the exception of student attainment) are based on self‐report from students and parents, which may lead to some measurement error. Recent policy changes, such as the increase in the student fees cap from 2012, may have an effect on students’ subject choices; something that cannot be assessed in the current Next Steps cohort.

Acknowledgements

I would like to thank Alice Sullivan, Lindsey Macmillan and George Ploubidis for guidance and advice in writing this paper, and Morag Henderson and Andrew Jenkins for helpful comments on a preliminary draft. I would also like to thank two anonymous reviewers for taking the time to offer comprehensive and insightful suggestions. This work was made possible by funding from the Economic and Social Research Council (ESRC). Finally, I am grateful to the Centre for Longitudinal Studies, UCL Institute of Education for use of these data and to the UK Data Archive and UK Data Service for making them available. However, they bear no responsibility for the analysis or interpretation of the data.