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Unequal AttainmentsEthnic educational inequalities in ten Western countries$

Anthony Heath and Yaël Brinbaum

Print publication date: 2014

Print ISBN-13: 9780197265741

Published to British Academy Scholarship Online: May 2015

DOI: 10.5871/bacad/9780197265741.001.0001

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Academic and Vocational Tracking in Upper Secondary Education

Academic and Vocational Tracking in Upper Secondary Education

Chapter:
(p.119) 5 Academic and Vocational Tracking in Upper Secondary Education
Source:
Unequal Attainments
Author(s):

Laurence Lessard-Phillips

Yaël Brinbaum

Anthony Heath

Publisher:
British Academy
DOI:10.5871/bacad/9780197265741.003.0005

Abstract and Keywords

This chapter focuses on students who continue in full-time education after compulsory schooling and asks whether minority students are disproportionately channelled into lower-status vocational tracks and are excluded from the high-status academic tracks which lead to higher education. The picture that emerges is of distinct patterns in different sets of countries. In Belgium, Germany and the Netherlands, minority groups are less likely to follow the academic track, but this under-representation can be entirely explained by their disadvantaged socioeconomic backgrounds and relatively low grades in lower secondary school. However, in a second group of countries – England and Wales, Finland, France and Sweden –most minorities are in fact more likely to follow the academic track than their majority-group peers from similar socioeconomic backgrounds and with similar grades. The indications are that comprehensive systems offer greater opportunities for minority students to fulfil their ambitions than do tracked educational systems.

Keywords:   academic track, vocational track, second generation, ethnic penalties, ethnic premia

Introduction

THE PREVIOUS CHAPTERS HAVE SHOWN THAT THERE ARE important differences between ethnic minorities with regard to test scores and attainment during compulsory schooling, as well as important differences in continuation rates into upper secondary education. Some of these differences can be attributed to variations in social background, yet not all. In this chapter we will explore the field of upper secondary education by investigating an important differentiating aspect at this education level, one that can have an important impact on the future educational and occupational labour market careers of the second generation: tracking into academic (or general) and vocational paths.2,3 On the one hand, vocational educational tracks may smooth the transition into the labour market. Yet, on the other hand, vocational education will not lead to high-status occupations, as is possible with more academic tracks, especially the ones leading to tertiary education (Müller 2005). Given this we wonder about the extent to which the second generation choose, or is channelled into, the different tracks and whether this differs from the majority group.

One possibility is that processes work very much in the same way for minorities as they do for the majority groups. That is to say, we might expect (p.120) to find that socioeconomic background and achievement at the end of lower secondary education will largely determine which track students follow in upper secondary schooling, with higher test scores or grades (and more advantaged social backgrounds) leading to entry into the higher prestige academic track, while lower test scores and more disadvantaged backgrounds will lead to vocational tracks, if they have not dropped out of school altogether. Social background has consistently been found to explain ethnic educational inequalities in many countries (for a summary, see Heath et al. 2008). On this line of argument, we would expect to find that most of the gross differences in entry to the different tracks can be explained by socioeconomic position and test scores, leaving little in the way of either ethnic penalties or ethnic premia. In other words, it is possible that the choice of track simply reflects the prior schooling of minorities and that there are no additional ‘ethnic effects’ over and above those generated earlier in the school career. This is represented by Figure 5.1.

In Figure 5.1(a), the arrow from socioeconomic status (SES) to achievement (measured by test scores or grades) represents what is often termed the ‘primary’ effect of social class on performance (as studied in Chapter 3). Sociologists of education typically think of this primary effect as reflecting processes of family socialisation and parental encouragement throughout the periods of pre-school, primary and lower secondary education which equip students with the skills to perform well at school. The arrow from achievement to academic track choice then reflects the relationship between test scores or grades and track choice, higher-performing students choosing, or being advised to enter, the academic track. Finally, the arrow from SES to the academic track then represents what is often termed the ‘secondary’ effect of social background. This secondary effect is often assumed to reflect aspirations for more prestigious careers, and the economic resources to be able to realise these aspirations (Boudon 1974; Halsey et al. 1980; Jackson et al. 2007). Research on social class inequalities has invariably found that both the primary and secondary effects of social background are positive, with middle-class children having a higher likelihood of scoring well on achievement tests than working-class children, and also being more likely than working-class children with similar achievement scores to continue with their educational careers. The primary and secondary effects of class are thus cumulative, increasing the advantage of middle-class children over the course of their educational careers.

Symmetrical with the primary and secondary effects of SES there can also be primary and secondary effects of ethnicity, as shown in Figure 5.1(b). The arrow from ethnicity to achievement represents the ethnic effects on tests or grades (again as studied in Chapter 3). The arrow from ethnicity to the academic track then represents the secondary effects of ethnicity, controlling both for test performance and for SES. Heath and colleagues have highlighted these (p.121)

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.1. Primary and secondary effects of social background and ethnicity: hypothesised effects.

secondary effects as potentially important when investigating ethnic differentials in education and also potentially operating in a different way from the secondary effects of SES (Heath & Brinbaum 2007; Heath et al. 2008). In particular, rather than being cumulative as with SES, the secondary effects of ethnicity may serve to mitigate adverse primary effects. Thus minorities' high educational and occupational aspirations (perhaps resulting from the positive selectivity described in Chapter 2) may lead them to make more ambitious choices in upper secondary education than their peers from the majority group.

(p.122) This approach fits within an alternative line of argument which suggests that some minority groups, especially perhaps the most positively selected ones such as the East and South Asians with high aspirations for their children, might continue to aim high and might have even higher rates of entering the academic track than their majority-group peers with similar backgrounds and grades. A number of studies have highlighted the role of educational aspirations for explaining the educational advantage of some minorities (for instance, Gupta 1977; Kao & Tienda 1995; Brinbaum & Kieffer 2009). Positive selection of the migrant (parental) generation may lead to high aspirations for the second generation to take advantage of the educational opportunities available in Western countries. Regardless of selection, it could also be the case that immigrant families strive for mobility and high occupational status in the host country and hence emphasise education as a means to achieve this (Zeroulou 1988; Van Zanten 1997; See et al. 2011) and attempt to make sure that discrimination on the labour market is avoided via the acquisition of more prestigious academic qualifications (Heath et al. 2008; Kilpi-Jakonen 2011). Academic tracks may thus be seen as an important stepping stone towards university, upwards mobility and avoidance of future discrimination in the labour market.

In France, for example, Vallet (1996) has attributed immigrant children's success to the higher educational aspirations of immigrant families. These high aspirations have been confirmed with another French source focused on family educational investment, furthermore showing important differences among minorities (Brinbaum 2002) and in particular between Portuguese and North African families, the two most numerous groups. Portuguese families have educational aspirations close to those of native French families with similar social backgrounds, being more likely to favour vocational studies, while North African families are more likely to aspire to the academic track for their children as a means towards greater social mobility. This latter group tends to avoid vocational studies and some of them express a feeling of injustice when they are directed towards vocational tracks (Brinbaum & Kieffer 2005; 2009; see also Brinbaum & Guégnard 2013). This pattern has also been found in the Netherlands, where previous research has shown that, overall, ethnic minorities within the Dutch school system tend to join the academic track in greater proportion than would be expected given their social background and performance (see, for example, Hustinx 2002; Van de Werfhorst & Van Tubergen 2007).4 Similar aspirational views also appear to be held by parents of South (p.123) Asian women in Britain, who see the academic track as the only suitable educational path to follow (Dale et al. 2002; Bagguley & Hussain 2007).

A third line of argument suggests that patterns may vary depending on the nature of the educational system and the stage at which assignment into the different tracks occurs (Kristen & Granato 2007; Crul & Schneider 2009), as exemplified in Figure 5.1(c). Here we show different secondary effects of ethnicity depending on whether the minority is resident in a country with early selection or not. This fits within the comparative context integration theory advanced by Crul and Schneider (2010), which emphasises the importance of tracking as one important factor shaping the outcomes of the second generation in European cities. As we noted in Chapter 2, the countries under study in this volume are quite varied when it comes to tracking. On the one hand we have the highly tracked systems of Germany, Belgium and the Netherlands, where tracking into specific vocational and academic streams occurs at the lower secondary level. In some of these systems it may be quite difficult to move between tracks, and the ‘choice’ of entering the academic or the vocational track may be largely determined by the track one was assigned to in lower secondary education. In addition, within these countries, the arrangements for assigning students to tracks will vary, with choice of track depending on parents' preferences together with teacher recommendations (Germany), test results (the Netherlands), or a mixture of the two (Belgium). Given the propensity for the children of immigrants to be overwhelmingly assigned to these tracks (such as in Germany and the Netherlands: see Alba 2005; Crul & Schneider 2009), tracking the second generation in upper secondary might prove more problematic.

On the other hand, we have the systems of England and Wales, Finland, France and Sweden where differentiation only starts at the upper secondary level. In these countries, selection into the different tracks is based on performance at school and teacher recommendations or in national examinations, in conjunction with students' and families' preferences. There are a number of potential reasons why later selection might affect the pattern of ethnic penalties or premia. First, if the argument about the ambition of the more positively selected groups is correct, such groups might work harder and make greater progress during lower secondary education in comprehensive schooling systems whereas they might be constrained in the systems with early tracking and might face greater barriers to moving upwards to more prestigious tracks. To be sure, this kind of mechanism would likely affect their achievements at the end of lower secondary (as suggested in Chapter 3). Second, systems with late selection, provided that they allow some degree of student or parental choice in the assignment to tracks and are not wholly driven by performance, may give greater opportunities for ambitious students to enter the academic track than systems where later tracks are largely (p.124) continuations of earlier ones. Moving between tracks in these early selection systems may be particularly difficult if the different tracks in lower secondary follow different curricula, whereas the common curriculum of some comprehensive systems may leave students greater freedom of choice. In other words, the key issue might be the extent to which students are constrained or are free to choose at the point of transition into upper secondary education (more or less dependent on their performance).

Therefore, we will investigate whether the timing of selection into the different tracks has a significant impact on second-generation differentials.5 Moreover, we will not only look into the impact of social background on tracking, just as in the previous chapters, but will also examine the extent to which prior school performance conditions the choice of track in upper secondary.6 Finally, we will investigate different ways in which one can explain ethnic educational differences in tracking.

The research questions are therefore as follows. Do we find a continuation of the patterns reported in Chapters 3 and 4, with some minorities over-represented in the high-prestige academic tracks and others in the lower-prestige vocational tracks? In other words, is there differentiation in tracking between the majority and minority, and between minority groups, just as there was in test scores and early school-leaving? Is this differentiation the same within the groups or are there any gender differences? Second, can such ethnic differences be explained largely in terms of socioeconomic background and test scores, or are there distinctive secondary effects of ethnicity (either ethnic penalties or premia) even after taking account of these factors? And third, do outcomes vary according to the type of the country's educational system, especially with regard to the timing of selection?

Origin and destination differences in academic and vocational tracks

We begin, as in previous chapters, by documenting the gross differences before taking account of differences in social background or test scores. We exclude those students who left school at the end of lower secondary education and focus on those who continued into upper secondary. As we saw in Chapter 4, with the exception of England and Wales, second-generation pupils tend to leave lower secondary education in greater proportion than the majority, but this is mostly explained by social background and performance. We are thus (p.125) dealing with some selection bias in our sample, as we are examining the educational outcomes of those who have made the transition. Many low-performing minority students will have already been taken out of the school system by then.

In each of the seven countries covered in this chapter we calculate the percentage of continuing students from each ethnic group who are enrolled in the academic track (rather than the vocational track). We have distinguished the countries with early selection (Figure 5.2(a)) from those with later selection (Figures 5.2(b) and 5.2(c)).

Figure 5.2(a) gives the pattern for our three countries with early selection. In Belgium, the proportion of students belonging to the majority population is particularly high in the academic track (82%).7 It varies a great deal among minorities but in every case minorities are less likely to be found in the academic track than is the majority group: the rate is the highest for the Italian second generation (71%) and decreases for the Moroccan second generation (65%) and for the youths with Turkish origin (55%, i.e. 27 percentage points lower than the majority). This closely parallels the picture for test scores that we saw in Chapter 3, where the Moroccan and Turkish second generation scored well below the majority group. It is also consistent with previous research which showed that ethnic minorities, especially Turks, tend to be over-represented in vocational training and under-represented in academic tracks (Leman 1991; Hermans 1995; Timmerman et al. 2003; Phalet et al. 2007; Pasztor 2008; Baysu & Phalet 2012; Crul et al. 2012).8

In Germany, the pattern is very similar to Belgium: the second-generation minorities are most commonly found in vocational tracks although almost half of the majority are also in vocational tracks (49%), reflecting the important place of vocational training and apprenticeship in Germany (Schneider 2008). However, even taking this into account, most minorities are even more likely than the majority to be found in the vocational track, especially the Italian second generation who have particularly low rates (23%) of transition to the academic track. Only the Polish minority (and the mixed group) have comparable proportions in the academic track as the majority group. These results also tend to be consistent with existing scholarship on the matter (Worbs 2003; Alba 2005; Kristen & Granato 2007; Crul & Schneider 2009; Crul et al. 2012).

In the Netherlands, we find that the gaps are considerably smaller. Young people both of Turkish and Moroccan background are under-represented in (p.126)

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.2(a). Percentage of respondents enrolled in academic track/vocational track, by group for early selection countries.

(p.127)
Academic and Vocational Tracking in Upper Secondary Education

Figure 5.2(b). Percentage of respondents enrolled in academic track/vocational track, by group for late-selection countries

(except Sweden).

(p.128)
Academic and Vocational Tracking in Upper Secondary Education

Figure 5.2(c). Percentage of respondents enrolled in academic track/vocational track, by group and for Sweden

(late-selection country).

(p.129) the academic track, just as in Belgium, though the gaps are not so large. But the young people of Caribbean background (from Suriname and the Antilles) actually have higher proportions in the academic track. For this group, then, we have an important departure from the picture for earlier chapters, where all Dutch minorities had lower test scores and higher drop-out rates than the majority group. This picture is consistent with the descriptive picture of Dutch ethnic educational inequalities reported by Van de Werfhorst & Van Tubergen (2007), Crul & Schneider (2009) and Crul et al. (2012).

Turning next to the countries with later selection, in Figure 5.2(b) we find a very similar pattern in France to the Dutch pattern: French second-generation youths are less likely to be enrolled in academic tracks than the majority population. About 55 per cent for the youth with Portuguese and North African origins and 59 per cent for the Sub-Saharan African second generation are in the academic track, against 66 per cent for the majority group. The figure for the young people of Dom-Tom and French born-abroad origins is higher still at 72 per cent. These results are consistent with the evidence from the previous (Vallet 1996) and the more recent (Brinbaum & Kieffer 2005; 2009; Brinbaum & Cebolla-Boado 2007) educational panels.9

In England and Wales, we also find some groups over-represented and none significantly under-represented in the academic track, which is consistent with recent research on the topic (Jackson 2012; Jackson et al. 2012). The Indian and Chinese second generation have high rates of transition into the academic track (respectively 83% and 87%), considerably higher than the majority group (70%). The black African (74%) and Pakistani (70%) groups are similar to the majority, while second-generation Bangladeshi and black Caribbean youth have slightly lower rates (about 64% and 65%), although the differences are not statistically significant. So in England and Wales none of the main minority groups has a significantly lower proportion in the academic track than the majority group.

In Finland, whereas the evidence is scarce apart from research by one of this volume's contributors (Kilpi-Jakonen 2011), the picture is almost equally positive. Most of the minorities either have similar rates to the majority, or even are more likely to be in academic tracks. This is a striking contrast with the story for prior achievement, where all Finnish minorities performed less well than the majority group. The highest rates in the academic track concern the second-generation Europeans (72%) and Sub-Saharan Africans (77%). The North African second generation as well as those from East Europe are close to the majority while the lowest proportion is among the ex-Yugoslavs (19%). Students from this latter group constitute an exception, with their very high proportion in vocational school compared to the majority. They differ (p.130) from all other groups in their participation in vocational education, which might be due to their migration status (see Chapter 2).

In Figure 5.2(c), Sweden presents a similar pattern to Finland, with most ethnic minorities having similar or even higher transition rates to the academic programme than students belonging to the majority group. This holds true for most groups from European countries (with a high rate for the East European and Polish second generation), and from Middle East countries or Asian countries (whatever the specific origin). The rate for the North African second generation is particularly high (71%) and relatively high for the Turkish second-generation youth (compared to the other countries). The only groups with significantly lower proportions than the majority in the academic track are those with Nordic backgrounds—from Denmark, Finland and Norway. As with Finland, this is in contrast with the picture for grades at the end of lower secondary schooling where most minorities performed less well than the majority group. Again, the picture is consistent with results reported by Jonsson & Rudolphi (2010), Crul et al. (2012) and Jackson et al. (2012).

Hence we find a variety of patterns among our seven countries. In the case of Belgium and Germany minorities are predominantly under-represented in the academic track, mirroring their lower test scores at the end of lower secondary education. At the other extreme in England and Wales, Finland and Sweden most minorities are equally or even more likely to be found in the academic track, despite their lower average achievements at the end of lower secondary schooling.

Brief foray into gender differences

Gender differences in tracking will be discussed in detail in Chapter 8 but it is important to note that there are some notable gender differences in the track followed across minority groups. Table 5.1 highlights the odds ratios of female pupils being in the academic track rather than the vocational track, in comparison with male pupils within the same group.10 Here we only concentrate on very high (or very low) odds ratios, indicating important gender differences in tracking.

If we examine the odds ratios for all countries, we do not see many striking gender differences in academic track enrolment across groups. In fact, most minority groups follow the gendered pattern present in the majority (p.131)

Table 5.1. Within-group odds ratios of women being in the academic track in comparison with men.

Academic and Vocational Tracking in Upper Secondary Education

(p.132) group, where female pupils are more likely than male pupils to be in the academic track. However, some minority groups have much higher female participation in the academic track than found among the majority group,11 such as female pupils of the former Soviet Union and Turkish origins in Germany; of Moroccan origin in the Netherlands; Bangladeshi, Indian and Chinese origins in England and Wales; European origin in Finland; Maghrebian origin in France; South European, other Asian, Iranian and Iraqi origins in Sweden. Some minority groups in Finland lag behind in terms of female enrolment in the academic track, despite positive odds ratios (Russians and Estonians, East Asians, Sub-Saharan Africans, and West Asians and North Africans) but are still more likely to be in the academic track than their male counterparts.

There are, however, some exceptions. Some female groups have lower likelihood of academic track attendance. These include Turkish females in Belgium, Italian and ex-Yugoslav females in Germany, females from the mixed group in England and Wales and ex-Yugoslav females in Finland.

This brief examination of the odds ratios of academic track attendance shows us that an interesting gendered pattern seems to be at play, one which seems to revolve around greater opportunities for female pupils, especially from typically Muslim groups. Even if these groups might not necessarily have the same track attendance as their female peers in the majority group, they do, however, outperform their male minority peers (see for instance Jonsson & Rudolphi (2010) for Sweden; Brinbaum & Kieffer (2009), Brinbaum et al. (2012) for France). As we will see in Chapter 8, which covers the relevant literature and analyses in more detail, this is related to the better overall attainment of female pupils across the school career and their wish for emancipation.

The role of parental social background and prior test attainments

We expect to find that socioeconomic background and prior achievements (as measured by test scores, grades or exam results) are likely to be important influences on the probability of entering an academic track, although the analysis of the previous section suggests that this will not be the whole story, particularly in countries such as Finland and Sweden where most groups have higher transition rates into the academic track than the majority. Our main question in this section, then, is whether ethnic differences in enrolment rates in the academic track can be explained (partly or) largely in terms (p.133) of background and attainment. For which groups and in which countries do we find net ethnic penalties or premia?

We therefore run a series of probit regression models. As in the previous section, we exclude students who had already left school and focus on the probabilities of continuing students entering the academic rather than the vocational track. We run three models for most countries (only two for Belgium and Germany).12 In the first model we simply run a model which distinguishes the different ethnic groups. This first model essentially presents the same information about proportions in the different tracks as in the section above, but now expressed in terms of probit coefficients in order to make them comparable with the results from the second and third model. In the second model we add controls for gender and socioeconomic background (parental social class, parental education and family composition). In the third model, we add the measures of achievement in lower secondary education, when available. We show the country-by-country coefficients in Figures 5.3(a)5.3(g). For each ethnic group, the upper bar shows the gross effect while the lower bar shows the ‘net’ effect after controls for socioeconomic background. Unshaded bars indicate ones where the coefficient is not significantly different from zero, i.e. where the minority is similar to the majority group.

Figure 5.3(a) shows the results for Belgium. As we can see, in the first model the academic track is a path less chosen by the second-generation Italians, Moroccans and Turkish, given their negative significant coefficients. Yet, for the first two groups these ethnic differentials are wholly explained by their social background. Social background also explains a large part of the Turkish disadvantage, although the members of this group remain significantly less likely to choose the academic track compared to the majority population with similar socioeconomic backgrounds. Even for this group, however, the gross disadvantage is much reduced in model 2 and the remaining ethnic penalty is relatively small.

Germany was the country that came closest to Belgium in the analysis of gross differentials, with young people of Turkish, Italian, former Soviet and former Yugoslav backgrounds all being under-represented in academic tracks, while those of Polish background were similar to the majority group. We can see this clearly in model 1 of Figure 5.3(b). In model 2, however, we see that socioeconomic background wholly explains all these disadvantages. None of the groups is significantly under-represented in the academic track once we take account of social background.

The Netherlands was also fairly similar to Belgium and Germany with respect to proportions of minority pupils in the academic track. As model 1 (p.134)

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(a). Gross and net second-generation effects on academic tracking, Belgium.

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(b). Gross and net second-generation effects on academic tracking, Germany

(p.135)
Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(c). Gross and net second-generation effects on academic tracking, the Netherlands.

in Figure 5.3(c) shows, the groups with Moroccan and Turkish background were significantly under-represented in the academic track. However model 2 shows that this under-representation is wholly explained by socioeconomic background, while taking account of prior test scores (taken at age 12) leads to a significant positive coefficient in both cases. In other words these two groups are more likely to be in the academic track than are their contemporaries from the majority group with similar backgrounds and test scores. Similarly for those of Antillean or Surinamese backgrounds, adding the controls increases the size of the positive ethnic coefficients. What this means is that, comparing young people from similar socioeconomic circumstances and similar test scores, all minorities are more likely to be found in the academic track than is the majority group.

Turning to France in Figure 5.3(d), we saw earlier that most second-generation groups were more likely to be in the vocational track compared with the majority group. However, just as in the case of Germany and the Netherlands, these negative differentials are all explained by social background. Prior performance also has an important impact on track choice: controls for prior test scores suggest that minorities are significantly more likely to be found in the academic track than are their majority-group peers with similar performance levels. (p.136)

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(d). Gross and net second-generation effects on academic tracking, France.

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(e). Gross and net second-generation effects on academic tracking, England and Wales.

(p.137) The picture was somewhat different in England and Wales, where there were no significant gross disadvantages in track choice in the first place. However, as we can see from Figure 5.3(e), the coefficients tend to become even larger and more positive when we control for social background. On the other hand, adding controls for prior performance in model 3 does not make much additional difference, probably because some of the groups such as the Chinese and Indians were already outperforming the majority group in lower secondary education.

Figure 5.3(f) shows that a more or less similar pattern to France and England and Wales exists in Finland. In terms of gross effects (model 1), several groups were already more likely to choose the academic track than were the Finnish majority group. In most cases, these positive differentials become even larger as we move through the models. The negative second-generation differential in the choice of academic track apparent for the ex-Yugoslavs in model 1 also largely disappears once performance, in addition to social background, is taken into account.

Finally, the Swedish results, separated into two graphs in Figures 5.3(g) and 5.3(h) given the sheer number of second-generation groups, show that the picture is also quite significantly positive for the second generation and confirm the above-mentioned trends, with some trends similar to those of Finland, France and England and Wales. Even in model 1 (the gross effects of ethnicity), apart from the Nordic groups, most minorities had similar—or even

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(f). Gross and net second-generation effects on academic tracking, Finland.

(p.138)
Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(g). Gross and net second-generation effects on academic tracking, Sweden.

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.3(h). Gross and net second-generation effects on academic tracking, Sweden.

(p.139) higher—rates of enrolment in the academic track than the majority. Controls for social background and prior performance further increase the magnitude of the ethnic effects for these groups, and they fully explain the negative differentials for the Nordic groups.

Hence, if we are to summarise the results shown in this section, we see that, unsurprisingly, social background plays an important role in reducing any negative ethnic differentials that were evident in model 1 with regard to track choice in upper secondary education. Indeed the only example of an ‘ethnic penalty’ after controlling for social background is that of the Turkish minority in Belgium. In every other case, social background accounts for any disadvantage. Furthermore, once we take account of prior test scores and grades, we find that many minorities have higher chances of entering the academic track than their respective majority groups. In other words, the dominant picture is one of ethnic premia, not ethnic penalties, when it comes to track choice. Indeed, we find ethnic premia for Caribbeans in the Netherlands, for Portuguese, Maghrebians and Sub-Saharan Africans in France, for Africans, South and East Asians in England and Wales, for Russians, East Asians and Sub-Saharan Africans in Finland and for almost all groups in Sweden apart from the Nordic ones. This shows the presence of positive secondary effects of ethnicity on tracking in upper secondary education.

Gross and net effects of ethnicity: a comparative picture

The graphs of Figure 5.4 provide an overview of the net ethnic differences or ethnic penalties after controlling for social background. They show that, when it comes to being in the academic track, the biggest second-generation advantage is among the East Asian second generation in a consistent manner, and among the South, Southeast and other Asian second generation, but to a lesser extent. There is quite a lot of variation in the choice of track among the Western, Southern and Eastern European, as well as among the Turkish, African and Caribbean second generation, with the destination country effect seeming stronger than the origin effect. We should err on the side of caution with regard to these conclusions, but we do show that there seems to be variation in the outcomes with regard to the country of destination.

The picture is more consistent when looking at the second-generation effects net of prior school performance. For the most part, however, second-generation premia are present and the most striking aspect of Figure 5.5 is that these premia are smallest for the groups originating from Western Europe and to some extent from Eastern Europe too. In other words, the processes involved in allocation to tracks seem to work fairly similarly for the (p.140)

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.4. Second-generation differentials in a comparative perspective: net effects (social background).

(p.141)
Academic and Vocational Tracking in Upper Secondary Education

Figure 5.5. Second-generation differentials in a comparative perspective: net effects (performance).

(p.142) majority group and the other European groups, while many (though not all) non-European minorities exhibit major ethnic premia in track choice.

In terms of overall similarities and differences between countries, Figure 5.5 shows quite similar patterns in France, England and Wales and Sweden, where the story appears to be more positive on average, with some exceptions; and the same can also be said about the second generation in Finland. In Belgium, Germany and the Netherlands, the negative patterns seem to be almost all accounted for by parental social background. Yet, the second-generation premia found in the latter set of countries is not as strong as in the former three.

This cross-national picture of ethnic premia or the absence of ethnic penalties in track choice is a striking finding, which might appear counterintuitive to the overall picture presented at the descriptive level. It should be noted that similar findings have been made by a number of scholars independently for some of the individual countries covered here. In fact, similar conclusions have been reached in Belgium by Phalet et al. (2007); in England and Wales by Bradley & Lenton (2007), Jackson (2012), and Jackson et al. (2012); in Finland by Kilpi-Jakonen (2011); in France by Vallet & Caille (1996), Brinbaum & Cebolla-Boado (2007) and Brinbaum & Kieffer (2005; 2009); in Germany by Kristen & Granato (2007), Kristen et al. (2008) and Luthra (2010); and in Sweden by Jonsson & Rudolphi (2010) and Jackson et al. (2012). We can therefore have some confidence in the results (although we should emphasise that many previous scholars, most of them involved in this project, have used the same datasets as we have used for this chapter).

Discussion

This chapter's results contrast greatly with the analyses of school performance in Chapter 3 and show that, in general, the second generation is as or more likely to choose (or to be channelled into) the academic track than the vocational one, especially when compared with their majority group contemporaries with similar socioeconomic backgrounds. As for between-country differences, similar patterns are found in France, England and Wales, Finland and Sweden, where the likelihood of the second generation choosing the academic track is much higher, especially after adding the controls. In Belgium, Germany and the Netherlands, negative gross ethnic inequalities in the choice of academic track mostly disappear once social background is taken into account.

This fits within the third framework that we established at the beginning of this chapter (Figure 5.1(c)). On the one hand, we find a lack of ethnic secondary effects in the early selection countries and among European minorities, yet (p.143) on the other hand there appear to be important secondary effects of ethnicity at play in late-selection countries for non-European minorities. As mentioned, these could include high parental and student aspirations; the value that immigrant families place on academic tracks compared to vocational ones as well as the promise of occupational success that these tracks lead to, and/or attempts at avoiding future discrimination in the labour market. This is not to say that the lack of ethnic effects in early selection countries, where selection is an important sorting mechanism, implies that they do not exist, but that they might well be intertwined with overwhelmingly important social background effects.

Given the comparative perspective of this chapter, it is difficult to take all details into account, but in some countries there is a hierarchy within the academic/vocational tracks that is also important to consider. For instance, in France the academic track is composed of the general and technological tracks. Within each academic track there are different fields of study as well as various specialties within the vocational track; the choices made with regard to these tracks have consequences for participation in higher education. Research has also shown that second-generation youth are less likely to be in the more highly valued general track; that they take more time to reach those tracks; that their drop-out rates from the academic tracks are higher; and that even choosing the more constraining vocational tracks reduces their chances in the labour market (Modood 2003; Van de Werfhorst & Van Tubergen 2007; Brinbaum & Kieffer 2009; Brinbaum & Guégnard 2013). In England and Wales, the choice of courses selected in upper secondary education may also have an important impact on entry into tertiary education. Needless to say, analyses focusing on more in-depth study of the differentiation within tracks are warranted to get a more accurate picture of the situation.

In explaining the ethnic differences, especially the high track placement of the Asian and African groups, the same kind of explanation suggested in Chapter 3, namely a focus on the positive selection of the parents' generation and the high aspirations that they have for their children, seems very plausible. It is noteworthy that the groups which show the largest ethnic premia in track placement tend to be ones which Chapter 2 indicated were positively selected, while some of the groups such as the Nordic groups in Sweden, who did not exhibit premia, were negatively or neutrally selected.

This is well exemplified in Figure 5.6, which plots the ethnic coefficients (net of social background) against the level of group selectivity and shows the relationship between the two. The scatterplot nicely confirms the above conclusion, in that positively selected groups will generally tend to exhibit the largest ethnic premia. Hence selectivity could be argued to play an important role in the types of educational choices that the second generation (and their (p.144)

Academic and Vocational Tracking in Upper Secondary Education

Figure 5.6. The relationship between tracking (net effects) and selectivity.

parents) make, above and beyond the role played by socioeconomic resources. There thus seem to be important secondary effects of ethnicity at play with regard to tracking.

In explaining the cross-national differences, we need to think of institutional arrangements, particularly the stage at which tracking first occurs, which might have an impact on differentiated results at the national level. The natural explanation for these different patterns, as we explained at the beginning of the chapter, is the age at which tracking starts. Belgium, Germany and the Netherlands are all countries where tracking starts at relatively early stages of the school career, whereas in England and Wales, Finland, France and Sweden it does not really begin until the upper secondary stage of schooling. As we argued, early tracking may well mean that students are more constrained in the choices that they can make: once one has been in a particular track for several years, it may be quite difficult to switch to a different curriculum. In contrast, if tracking is postponed until the upper secondary stage, students may have more freedom to make their own choices—in effect they will be less constrained by their educational history. Students from minority backgrounds in particular may be more inclined to aim high, perhaps because of the ‘immigrant optimism’ of their parents that a number of scholars such as Kao & Tienda (1995) have noted. So where they are free to choose, they opt for the more ambitious option.

(p.145) On similar lines Waters et al. (2013) have contrasted situations where minorities can choose with those where they are chosen. Where minorities are ‘free to choose’ the positively selected groups may be more likely to aim high for the academic track, whereas where choices are constrained, ambition may make less difference. That is to say, we may find that the degree of selectivity may have larger effects in countries that offer more choice. While we do not have sufficient cases to permit a rigorous test of this hypothesis, inspection of Figures 5.3(a)5.3(h) does suggest that there is less variability in the size of the ethnic premia (which also tend to be relatively modest) in the more constrained systems of Belgium and Germany, and more variability in the less constrained systems. Whether these educational choices will prove a good strategy for later stages of the educational career still requires investigation; this will be explored further in Chapters 6 and 7.

Proceedings of the British Academy, 196, 119–148. © The British Academy 2014

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Notes:

Proceedings of the British Academy, 196, 119–148. © The British Academy 2014.

(1) Nadia Granato kindly prepared the German Mikrozensus and ran the analyses for Chapters 5, 6 and 7. She is a researcher at Mannheimer Zentrum für Europäische Sozialforschung (MZES), Universität Mannheim.

(2) Academic tracks prepare for entry into tertiary education, whereas vocational tracks prepare for specific trades or entry into the labour market. For a more in-depth discussion about differences in the tracks between the countries, see Chapter 2.

(3) We should note that explicit tracking is not present in the two North American systems and hence we do not include the USA or Canada in this chapter. We also have to exclude Switzerland as we do not have access to the necessary data to tackle the key questions.

(4) Some attribute the over-representation of ethnic minority pupils in the academic track to teachers ‘over-recommending’ the academic track to ethnic minority pupils (although this measure appears to have disappeared in recent years; see Driessen 2011).

(5) We will also briefly emphasise the gender-component aspect of tracking choice, in order to set the path for Chapter 8.

(6) For more details on the measures of performance, see Table A3.2 in Chapter 3.

(7) Note that in Belgium our measure of track is based on data about the track completed in the Census, not track entered.

(8) The results from Crul et al. (2012) come from data at the city-level, but seem to confirm the descriptive trends observed in the national data under examination.

(9) The data used in this volume is the most recent education panel available.

(10) Odds ratios greater than 1 indicate that women are more likely than men to be enrolled in an academic track than a vocational track. Odds ratios smaller than 1 indicate that women are less likely than men to be enrolled in the academic track. Odds ratios of 1 (or close to 1) indicate there is no difference between women and men in enrolment in the academic track.

(11) In most instances, however, women from the majority group tend to be more likely to be enrolled in the academic track than their minority peers (specific odds ratios not shown).

(12) Test scores are not available in the datasets used for Belgium and Germany; in both instances only the first two models are presented.