doi: 10.1111/j.1467-954X.2007.00715.x
Making education count: the effects
of ethnicity and qualifications on
intergenerational social class mobility
Lucinda Platt
Abstract
This paper examines the role of social class and ethnic group background in determining individuals’ social class destinations. It explores the extent to which these
background factors are mediated by educational achievement, and the role of
educational qualifications in enabling intergenerational class mobility. To do this, it
uses the ONS Longitudinal Study. These data allow us to observe parents’ characteristics during childhood for a group of children of different ethnic groups growing
up in England and Wales in the same period and who had reached adulthood by
2001. Results show that the influence of class background on these children’s subsequent social class position varied with ethnicity: it was important for the majority,
even after taking account of educational qualifications, but had a much smaller role
to play for the minority groups. The minority groups made use of education to
achieve upward mobility, but to greater effect for some groups than for others.
Among those without educational qualifications, minority groups suffered an ‘ethnic
penalty’ in relation to higher class outcomes; but for Pakistanis and Bangladeshis,
this penalty persisted at all levels of education. These findings challenge the notion
that a more equal society can be achieved simply through promoting equality of
opportunity through education.
Introduction
The extent to which Britain is an open society has been a major concern of
sociological research for a number of decades (Glass, 1954; Halsey et al.,
1980; Goldthorpe et al., 1987; Marshall et al., 1997; Heath and Payne, 2000;
Gershuny, 2002). Attention has focused both on the role of class background
in determining future life chances, and also on how that advantage is maintained through, or disrupted by, the increasing role of educational qualifications in determining occupational position (Goldthorpe, 1997, 2003).
Aspirations towards a meritocracy based on educational achievement (Blair,
2001) appear hard to achieve. However, class advantage and educational
success do not necessarily operate to the same effect across groups. Ascertaining how different forms of advantage or disadvantage intersect is inforThe Sociological Review, 55:3 (2007)
© 2007 The Author. Journal compilation © 2007 The Editorial Board of The Sociological Review. Published by
Blackwell Publishing Inc., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, 02148,
USA.
Lucinda Platt
Chinese
No Academic
or Professional
Qualifications
Other Black
Black African
1+ O level or
equivalent
Black Caribbean
5+ O levels
or equivalent
Bangladeshi
2+ A levels or
equivalent or
higher
Pakistani
Indian
Other
Qualifications
White British
0%
10%
20%
30%
40%
50%
60%
70%
80%
90% 100%
Figure 1 Highest level of educational qualifications across selected ethnic
groups, 2001, all aged 16–74, England and Wales
Source: 2001 Census, Commissioned Tables.
mative both about the nature of stratification and equality within a society
and can help us to understand the processes by which some groups and not
others achieve success – and whether these processes are particular or more
general.
While class immobility is generally taken to indicate a closed (or ‘nonmeritocratic’) society, taking account of ethnicity complicates that understanding (Hout, 1984; Platt, 2005a). If minority groups reflect equivalent immobility
in their own class transitions, then this may be indicative of a form of parity at
the level of ethnicity even if not of class background. By contrast, if minority
groups show much more mobility up and down the class structure, then that
may give us cause for concern about the extent to which advantage achieved
by one generation can be sustained into the next, and thus lead us to question
equality of opportunity across ethnic groups.
Educational achievement is clearly crucial to minority group success. The
achievement of higher levels of qualifications across generations is a feature of
most groups’ experience, even if there remains an educational ‘deficit’ or
penalty for some groups. Figure 1 illustrates the variation between groups in
the distributions of their highest qualifications. This is echoed in the results for
16–24 year olds (Figure 2), though there are fewer with no or limited qualifications for all groups given the increase in qualifications for younger
generations.
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Making education count
Chinese
Other Black
No Academic
or Professional
Qualifications
Black African
1+ O level or
equivalent
Black Caribbean
5+ O levels or
equivalent
Bangladeshi
2+ A levels or
equivalent or
higher
Pakistani
Other
Qualifications
Indian
White British
0%
10%
20%
30%
40%
50%
60%
70%
80%
90% 100%
Figure 2 Highest level of educational qualifications across selected ethnic
groups, 2001, aged 16–24, England and Wales
Source: 2001 Census, Commissioned Tables. Crown Copyright.
Accounts attempting to explain the situation of what are commonly
regarded as the more successful of the UK’s minority ethnic groups (Indian/
East African Asian and Chinese) have often stressed a particular groupspecific attachment to education (Modood, 2004; Archer and Francis, 2006).
However, commitment to education is also well-attested among groups that
achieve less educational success. The higher rates of staying on in school
among minority groups, including ‘less successful’ minority groups such as
Caribbeans and Pakistanis, suggest that minority group members are well
aware of the importance of education as a necessary (if not sufficient) route
to success, and that they are highly motivated (DfES, 2004; Heath and Yu,
2005).
Moreover, the relationship between educational qualifications and occupational outcomes is not necessarily straightforward. Levels of educational qualifications across groups may help explain patterns of occupational success; but
they are insufficient to explain unemployment rates as they differ by ethnic
group (Heath and Yu, 2005). Excess unemployment among minority ethnic
groups persists even when education is controlled for (Blackaby et al., 1999).
This finding is supported when UK-born are distinguished from those born
outside the UK (Blackaby et al., 2005) and when background factors that may
provide additional protection against unemployment are taken into account
(Platt, 2005b). And lack of motivation among those who fare less well in
employment is not supported by the evidence (Thomas, 1998).
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Lucinda Platt
Variation in educational qualifications, then, only accounts for part of the
differences in social class outcomes between groups. Some would attribute the
remaining difference to discrimination (e.g. Blackaby et al., 1998). However,
that not only assumes that all other relevant factors have been captured in the
models, but, with its stress on individual level discrimination, discounts a role
for structural processes perpetuating disadvantage, that may or may not be
ethnically specific. Heath and McMahon (1997), in coining the term ‘ethnic
penalty’, were more cautious, arguing that it contains both discrimination and
further, unmeasured, characteristics that vary with ethnicity. One of the
unmeasured characteristics they posit is parental class background. This brings
us to the role of class background in determining or influencing both educational and occupational success.
The few studies that have been carried out on ethnic differences which take
account of class background indicate that class origins are important for
minority groups but that an ‘ethnic penalty’ remains – at least for some groups
(Heath and Ridge, 1983; Heath and McMahon, 2005; Platt, 2005a). However,
the former two studies were problematic in their measurement of origin class
being predominantly pre-migration, while Platt (2005a) did not consider education. It is also important to identify whether educational qualifications
operate consistently across groups and whether ethnic penalties can be found
at all levels of educational achievement.
The analysis presented here, therefore, takes account of background factors,
including parental class, that are measured in the same country (England/
Wales) and educational system and in the same time period and across a range
of ethnic groups. By investigating the intersection of class and ethnic background and the role of education it aims to refine our understanding of
mobility processes and of the diverse fortunes of Britain’s ethnic groups.
Data and study design
The data for this paper come from the ONS Longitudinal Study, a recordlinkage study of one per cent of the population of England and Wales. It was
initially obtained by taking a sample of the 1971 Census, based on those born
on one of four birth dates (day and month). At each subsequent Census,
information from samples taken using the same sampling criteria is linked
where possible. Between censuses, members are added to the study by linking
information on births and immigrations, again using the same sampling criteria. Information on death and emigration from England and Wales is also
linked to the study. No further information is linked for sample members who
have died or emigrate, although linkage recommences for members who
return to England and Wales.
The paper examines two combined cohorts of LS members, those who were
children aged 4–15 in 1971 and those who were aged 4–15 in 1981. The pooling
of two cohorts was undertaken primarily to increase sample sizes of the
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Making education count
minority groups. The children’s origins, both in terms of social class origins and
other family/parental characteristics, were measured in 1971 or 1981, since the
characteristics of co-resident household members and their relationship to the
study member, in this case parents, are included in the LS at each wave. Their
achieved class position was measured at 2001 (when they were aged between
24 and 45). Thus, this analysis benefits from a prospective design: measurement
of origins is observed directly and is therefore not subject to recall error and,
particularly important for minority ethnic groups, is known to be their postmigration situation in England and Wales rather than that prior to migration.
Variables constructed to summarise characteristics associated with study
members’ origins (when they were children in 1971/81) were:
• Parents’ social class: using the three-category CASMIN schema, that is
service (the highest social class), intermediate, working, and other (where
respondents did not fit one of the former classes). Where both parents
were co-resident with the study member and occupied different occupational class positions, the higher of the two was allocated as the parental
social class (the ‘dominance approach’);
• housing tenure (owner occupation, local authority housing, private
rented);
• car ownership in household (0 cars, 1 car, 2 or more cars);
• parents’ qualifications (no mother (father), mother (father) has no higher
qualifications, mother (father) has higher qualifications).
At 2001, variables constructed to measure study members’ own characteristics
were:
• social class: according to the NS-SeC (the 2001 equivalent of the
CASMIN schema). The dominance approach was again used to allocate
destination class where the study member and a co-resident partner
occupied different class positions. The implications of this approach for
the possibility of analysing outcomes for men and women separately are
discussed below. This study focuses on attainment of professional / managerial class positions compared to any other outcome (including unemployment or inactivity).
• ethnic group: based on the 1991 ethnic group classification, but modified
to differentiate those of migrant parentage from those of non-migrant
parentage. The minority groups were required to have at least one
migrant parent and the white majority group was required to have both
parents UK born. In addition, a white migrant group was created based
on white 1991 ethnicity combined with both parents being migrants. A
residual category comprised those who did not fit these combinations of
(non)migrant parentage and ethnicity.This paper concentrates on the five
largest groups, for which discrete analysis is possible and meaningful,
namely: white non-migrant, Caribbean, Indian, Pakistani and white
migrant ethnic groups.
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Lucinda Platt
• partnership status (this was particularly important as the allocation of
class using the dominance approach meant that those who were partnered would be classified by their partner’s class if it was higher).
• education (recoded into four categories of highest qualification: none,
NVQ Level 1 (1–4 GCSEs grades A* to C or equivalent), NVQ Level 2
(5+ GCSEs grades A* to C or equivalent), NVQ Level 3 and above (2 A’
levels or equivalent, and above).
Variables to take account of whether origins were observed at 1971 or 1981,
the age of the study member in 1971 or 1981, grouped into three bands (4–7,
8–11 and 12–15), and the combination of these two variables as birth cohorts,
and the concentration of minorities in the ward where the study members
were growing up in 1971 or 1981, divided into two bands, low or high, were also
created and included in the analysis. These offered checks on the impact of
differential outcomes by age; on the possible period effects of measuring
parents’ characteristics at two different times – 1971 and 1981; and on some of
the effects associated with growing up in different areas or sorts of ‘neighbourhood’. These controls, while contributing to the fit of the models, did not reveal
note-worthy trends and therefore, while included in the models, are not
included in the discussion of results.
The sample was also differentiated by sex. However, because of the way
outcome class was constructed using the dominance approach, for those who
were partnered the class allocated was that of the couple rather than that of
the individual. This means that the sex variable only differentiated effectively
between those who were single in 2001. The approach to allocating class
position was adopted as it is class rather than occupation which is the outcome
of interest, and it is not meaningful to treat people’s class position as independent of their partner. By implication it therefore regards marriage as well as
individual occupational success as a potential route to upward mobility. The
approach does have the disadvantage, though, that gendered trajectories or
differences in the influence of background on outcome for men and women
cannot be clearly distinguished. It also means that those groups where women
are more likely to be economically inactive following marriage, in particular
Pakistanis and Bangladeshis (Dale et al., 2004), have immediately lower
chances of the female spouse raising the class position of the couple, with
implications for both partners. This is a point I return to when considering the
results.
Results
Effect of education on intergenerational class mobility
The distribution of the main variables across the five largest minority groups
considered in this analysis is shown in Table 1.1 The distribution across the
white non-migrant group is comparable to that for the sample, given their
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Making education count
Table 1 Percentage distributions (column %) of family characteristics at
origin in 1971/81 and own characteristics in 2001
White non- Caribbean
migrant %
%
Origin Characteristics:
1971/81
Origin (parents’)
Class: Service
Origin (parents’)
Class: Intermediate
Origin (parents’)
Class: Working
No co-resident mother
Mother with no higher
qualifications
Mother with higher
qualifications
No co-resident father
Father with no higher
qualifications
Father with higher
qualifications
Owner occupied
housing
Local authority housing
Private rented housing
No car
1 car
2 or more cars
Own characteristics:
2001
Destination (own)
Class: professional/
managerial
Destination Class:
Intermediate
Destination Class:
Routine/manual
Destination Class:
Unemployed and
Other
N = 2,110
Indian
%
Pakistani
White
%
migrant %
N = 2,005 N = 1,033
N = 4,480
29
13
13
7
18
19
9
13
16
19
52
78
74
77
63
100
2
90
100
4
87
100
2
94
100
5
93
100
5
84
8
9
4
2
11
100
8
78
100
21
74
100
5
84
100
5
90
100
18
72
14
4
11
5
9
100
57
100
50
100
84
100
87
100
56
33
10
100
28
55
17
100
N = 125,014
40
10
100
60
35
5
100
N = 1,547
9
7
100
44
47
9
100
N = 1,691
8
5
100
50
45
5
100
N = 779
32
12
100
48
42
10
100
N = 3,675
48
39
55
32
50
19
21
18
22
20
24
20
18
21
19
9
20
9
25
11
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Lucinda Platt
Table 1. cont.
White non- Caribbean
migrant %
%
No qualifications
Level 1 (1–4 GCSEs at
A* to C grades, or
equivalent)
Level 2 (5+ GCSEs at
A* to C grades, or
equivalent)
Level 3 and above (2
A’ levels plus, or
equivalent)
Group % of total
sample (row percent)
Indian
%
Pakistani
White
%
migrant %
100
15
29
100
11
30
100
10
20
100
20
21
100
14
26
25
26
19
19
25
31
34
51
40
35
100
100
100
100
100
92
1.1
1.2
0.6
2.7
Source: ONS Longitudinal Study, author’s analysis.
numerical dominance (over 90 per cent of the total sample). We can see that
far more children from white non-migrant backgrounds were growing up in
service class families than were children from minority ethnic groups in 1971/
81. Conversely those from minority group backgrounds were heavily concentrated in working class families as children. The transformation of the class
structure meant that almost half of the sample were living in professional or
managerial class families in 2001, though this varied by ethnic group with 55
per cent of Indians but only 32 per cent of Pakistanis in this position.
Few from any group had either father or mother with higher qualifications;
but Caribbeans and white migrants were more likely to be growing up without
a co-resident father, reflecting both particular patterns of migration, with
many Caribbean women migrating independently in the post-war era, and
post-migration living arrangements. Own qualifications in 2001 showed that
most groups had only a small proportion with no qualifications, though those
without qualifications amounted to one in five of the Pakistani sample. Those
with level three qualifications and above were around one in three among
white non-migrants, white migrants and Caribbeans, but rose to two in five
among Pakistanis and over half of Indians.
So what is the relationship between these characteristics? Can the class
outcomes be understood in terms of particular patterns of experience while
growing up: that is, experience of parental class, parental education and relative affluence (as proxied by housing tenure and car ownership)? Once we
take account of these relevant formative experiences do we cease to see
differences by ethnic group – or conversely does ethnic group ‘trump’ class in
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Making education count
its impact on life chances? Are educational qualifications able to turn around
class patterns of mobility, or are qualifications more an expression of advantaged backgrounds? To investigate these questions, binary logistic regression
models were estimated to ascertain the presence of ethnic group and class
origin effects and the extent to which they were modified by educational
qualifications (Table 2).
Model 1, which explored the relationship of background to professional or
managerial class outcomes without taking account of study members’ own
education, indicated class background was strongly associated with class outcomes, over and above the effect of having a highly educated parent or being
relatively well-resourced. These results accord with research that has shown
that class background remains important in determining life chances (Heath
and Payne, 2000). Additionally, it showed that nearly all the minority groups
experienced greater upward mobility (or retention in the higher class) relative
to white non-migrants with comparable backgrounds. This is indicated by the
positive and significant coefficients for these groups. This would support an
argument for these minorities having had their ‘true’ class position suppressed
in the first generation following migration to the UK. There has been some
evidence that migrants’ class position was depressed following arrival in
Britain, as a result of either discrimination or lack of familiarity with the
labour market, at around the time that parents’ class was observed in this study
(Daniel, 1968; Heath and Ridge, 1983). If that is the case, then what we might
be seeing is simply a reassertion of an underlying class position. Alternatively
(or additionally), this result would accord with the theory that the migrant
generation focuses motivation on the achievements of the second generation
(Archer and Francis, 2006; Card, 2005).
However, the Pakistani and Bangladeshi groups show the opposite pattern.
They experience less social class success than even their heavy concentration
in the working classes of the migrant generation (see Table 1) would lead one
to expect. Either their underlying class position is weaker than that of the
other minority groups, or they are subject to additional barriers or obstacles to
progress.
When study members’ own education is included (Model 2), the strong
reduction in the size of the positive ethnic group effects and their lack of
significance indicates that the upward mobility found in Model 1 is achieved
through the second generation gaining educational qualifications. This could
be explained through particular motivation to achieve associated with minority ethnic groups, mentioned above, or, once again, through ‘bounceback’ from
suppressed class position in the migrant generation, meaning that the level of
qualifications within lower class positions mimics those that might be expected
at higher class positions. These two aspects of aspirations and suppressed class
position might also intersect at the level of group norms, resources and
attitudes, sometimes conceived as ‘ethnic capital’ (Borjas, 1992), that both
reinforces a culture of aspiration and supports the upward mobility and educational attainment of the second generation.
© 2007 The Author. Journal compilation © 2007 The Editorial Board of The Sociological Review
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Lucinda Platt
Table 2 Logistic regressions of probability of professional/managerial
destination in 2001, controlling for individual and background
characteristics
Model 1
Coefficients (SE)
Ethnic group (baseline is white non-migrant)
Caribbean
0.306
Black African
0.469
Indian
0.460
Pakistani
-0.525
Bangladeshi
-0.274
Chinese and other
0.491
White migrant
0.318
Model 2
Coefficients (SE)
-0.037
-0.001
0.105
-0.792
-0.465
0.078
0.142
(0.076)***
(0.246)*
(0.068)***
(0.103)***
(0.245)
(0.106)***
(0.044)***
Sample member’s qualifications (base is 0)
Lower (level 1)
Middle (level 2)
Further (level 3+)
Origin class: base is working
Service class
0.547 (0.018)***
Intermediate
0.056 (0.017)***
Other
-0.204 (0.035)***
Mother’s qualifications (base no qualifications)
No co-resident mother
-0.198
Mother with qualifications
0.425
Father’s qualifications (base no qualifications)
No co-resident father
0.232
Father with qualifications
0.532
(0.081)
(0.268)
(0.069)
(0.111)***
(0.237)*
(0.109)
(0.047)**
1.013 (0.027)***
1.466 (0.027)***
2.765 (0.028)***
0.330 (0.019)***
0.012 (0.018)
-0.102 (0.038)**
(0.047)***
(0.025)***
-0.106 (0.052)*
0.119 (0.027)***
(0.029)***
(0.022)***
0.143 (0.032)***
0.220 (0.024)***
Tenure at origin (base is owner occupation)
Local authority
-0.576 (0.015)***
Private rented
-0.316 (0.022)***
-0.286 (0.017)***
-0.171 (0.024)***
Car ownership at origin (baseline is no cars)
1 car
0.277
2 or more cars
0.406
Constant
-1.101
LR chi2 (df)
12,928
Chi2 Change (df)
0.174
0.287
-2.430
22,448
9,520
N
(0.016)***
(0.022)***
(0.024)***
(28)***
(0.017)***
(0.024)***
(0.034)***
(32)***
(4)***
128,520
Source: ONS Longitudinal Study, author’s analysis.
Notes: *** p < 0.001; ** p < 0.01; * p < 0.05.
Standard errors are adjusted for repeat observations on persons.
The regression models were run (a) using dummies to represent missing cases for parental qualifications, own
qualifications and housing tenure and (b) excluding all cases with missing values. The advantage of the former
approach is that it maintains the sample size, however, it may do so at the expense of distorting the estimates
(Allison, 2002). Checks confirmed however that there was little difference between the estimates based on
exclusion of cases with missing values or on substitution of dummies for missing values. These estimates use
dummies, but for brevity the coefficients for the dummies are not given in this Table. Controls for age/cohort, sex,
partnership status and ethnic minority concentration in ward where living in 1971/1981 were also included but
are not reported here for concision. Full tables are available from the author on request.
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However, the effect for Pakistanis and Bangladeshis becomes stronger (a
more negative coefficient) once education is included, indicating that their
chances of professional managerial success are substantially worse than their
white non-migrant peers at the same level of education. If some sort of
underlying class position impacted on the achievement of qualifications
among the minority groups and thus determined the levels of professional
managerial class attainment, then we would expect to see lower qualifications
levels among these two groups. Consequently, their negative coefficient would
become weaker and non-significant, meaning that their lower chances of successful class outcome were similar to white non-migrants with comparable
levels of qualifications and similar backgrounds. Instead, the acquisition of
educational qualifications seems of smaller benefit to these minority groups.
Why should educational qualifications be less powerful for some groups
than for others? The barriers to success offered by discrimination is one
obvious reason; but this would not immediately explain why the impact was
borne particularly by certain minority groups. It may be that there is diversity
within institutions in their influence in the labour market: at the upper end,
attention has been drawn to the relative salience of a degree from Oxbridge
compared to a degree from a new university, for example (Shiner and
Modood, 2002). It may also stem from the greater level of support higher class
origins can give to staying in education after 18, given that the highest qualification level in this analysis combines A’ levels and degrees. A further contributory factor in this result is likely to be the inability of educational
qualifications to lead to higher occupational outcomes if withdrawal from the
labour market takes place. Thus if women with high qualifications withdraw
from the labour market following marriage to those in intermediate or routine
occupations, then their qualifications will not be congruent with their (couple)
class position. Nor will they be able to create upward mobility for their
partners. Given that Pakistani and Bangladeshi women are more likely than
women from other groups to withdraw from the labour market following
marriage (Dale et al., 2004), this is more of an issue for these groups. In
addition, girls from these groups may be more constrained in relation to the
higher education institutions they attend and subsequent opportunities (Dale,
2002). However, marriage patterns themselves are associated with educational
qualifications (Lindley et al., 2006), and examination of the differences
between individual and couple class outcomes (not illustrated) indicated that
while this factor played a role it could not explain more than a small part of the
observed ‘penalty’.
In addition, if ethnic capital can be important in enabling upward mobility,
it may also be relevant to our understanding of the more limited success of
certain groups. Certain groups may be less well resourced with Borjas’ type of
ethnic capital. Moreover, ethnic capital is often also conceived of as a form
of social capital; and within the social capital literature the distinction between
‘bridging’ and ‘bonding’ forms of capital, the one which eases upward mobility
the other which inhibits it, is often regarded as crucial. It is clear that the
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Lucinda Platt
relative role of bridging and bonding must vary at different points in the class
distribution (Lin, 2001). Those who start from a more disadvantaged position
may be ill-placed to secure upward social mobility. And this disadvantage may
be coterminous with high levels of concentration, with the Pakistani and
Bangladeshi minority groups being relatively highly concentrated compared
to other minority groups.
Both positive and negative attributes have been associated with so-called
‘enclaves’: they have been argued both to inhibit assimilation and thus upward
mobility and to provide a resource for minority ethnic groups. However,
Clark and Drinkwater (2002) failed to observe in Britain the positive effects
posited for enclaves. And Simpson has challenged the whole idea that the
more disadvantaged minority ethnic communities opt for ‘self-segregation’
(Simpson, 2004). At the same time, more successful minority group members
from certain groups may, in the process of expressing social mobility through
geographical mobility, select into co-residence in more prosperous areas
(Dorsett, 1998), and thereby facilitate the future mobility of their own and
others’ children. Individuals from certain groups may thus be better served by
geographical distribution and group histories as well as their own characteristics to exploit the potential of ‘bridging’ capital to effect upward mobility.
This would be consistent with the theory that ethnic penalties operate differently at different structural positions and with Portes and Zhou’s (1993) theory
of ‘segmented assimilation’.
To try to get a closer understanding of some of these issues of differential
impact, there are two lines of inquiry to pursue. The first is to examine the role
of class in more detail and whether it operates in a similar way across groups;
the second is to explore whether there are different ‘ethnic penalties’ at
different levels of qualifications. Two approaches are used: first the intersections of ethnicity and class and ethnicity and education are explored within the
main model; and second, separate models are estimated for the five main
ethnic groups.
Class background and ethnicity
The assumption of the models in Table 1 has been that the class effects operate
consistently across the values of the other variables, in particular across the
different ethnic groups. However, earlier research has suggested this may not
be the case (Platt, 2005a). The speculations raised in this paper both about
‘bounceback’ and about the role of ethnic capital have posited a situation in
which class background means different things for different groups. Given the
dominance of the white non-migrant majority within the sample, the class
effects observed in Table 2 will be driven by those that apply to this group,
disguising potential differences across the minority groups. Therefore, a set of
dummy variables was created, which combined the effect of class origin and
ethnic group, using white non-migrant of working class origin as the baseline
category. These were included in the model in place of the ethnic group and
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Table 3 The effects of ethnicity and origin class (at 1971/81) on the chances
of being in the professional or managerial classes in 2001
White non-migrant of service class origin
White non-migrant of intermediate class
origin
Caribbean of service class origin
Caribbean of intermediate class origin
Caribbean of working class origin
Indian of service class origin
Indian of intermediate class origin
Indian of working class origin
Pakistani of service class origin
Pakistani of intermediate class origin
Pakistani of working class origin
White migrant of service class origin
White migrant of intermediate class origin
White migrant of working class origin
N
Model 1
Coefficient (SE)
Model 2
Coefficient (SE)
0.559 (0.018)***
0.060 (0.018)***
0.341 (0.020)***
0.016 (0.019)
0.258
-0.106
0.410
1.00
0.476
0.451
0.151
-0.396
-0.461
0.674
0.419
0.361
(0.188)
(0.246)
(0.088)***
(0.189)***
(0.177)**
(0.080)***
(0.403)
(0.244)
(0.124)***
(0.106)***
(0.100)***
(0.056)***
-0.120
-0.277
0.128
0.486
-0.006
0.113
-0.511
-0.882
-0.654
0.205
0.305
0.166
(0.199)
(0.256)
(0.095)
(0.175)**
(0.181)
(0.081)
(0.369)
(0.253)***
(0.135)***
(0.114)
(0.110)**
(0.059)**
128,520
Source: ONS Longitudinal Study, author’s analysis.
Notes: as for Table 2.
class background variables. The coefficients for these dummy variables are
given in Table 3, focusing on just the three main origin classes and on the five
ethnic groups which form the focus of this paper.
If the effects of relatively privileged or relatively disadvantaged origins
were consistent across groups we would expect to see a positive and significant
effect for being of service class origin for all groups, and a statistically insignificant effect of being from a working class background for all the minority
groups. Instead, in the model without education, we see that for Caribbeans
the effects seem to work in the opposite direction, showing greater levels of
upward mobility from the working class, but with privileged class origins
having little bearing on subsequent outcomes. This impression is enhanced
when educational qualifications are taken into account, as the coefficient for
the higher two classes becomes negative (even if not statistically significant at
conventional levels). This points to an inability of those who grow up in more
privileged backgrounds to retain that advantage, which provides potential
evidence of discriminatory barriers to success. For Indians and white migrants,
successful outcomes are achieved relative to the white working class from all
origin positions, though the effect is graduated, suggesting that class background plays a role within the groups. And for Pakistanis, not only is there no
advantage (as for the Caribbeans) from advantaged origins, but those from
© 2007 The Author. Journal compilation © 2007 The Editorial Board of The Sociological Review
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Lucinda Platt
working class origins are less likely than their white non-migrant counterparts
to end up in professional or managerial positions. The large deficit among
those from working class backgrounds supports the notion that there is some
kind of segmentation, whereby those growing up in a relatively disadvantaged
position, which is not a consequence of class suppression to the same extent as
it is for other groups (Smith, 1977), face additional barriers in terms of class
mobility.
When education is included in the model (Column 2 of Table 3) we see,
as we did in Table 2, the way in which upward mobility for the Indians, white
migrants and, to a lesser extent, the Caribbeans is achieved through education. We can also see that, for these groups, class of origin plays a much more
circumscribed role, once its impact on educational qualifications has been
achieved. For the Indians and white migrants, then, there appears to be the
ability to retain class advantage through educational qualifications, while
those in less privileged class positions also achieve at unexpected levels to
gain good social class positions. This would fit with an idea of ethnic capital
as extending beyond individual circumstances and having a wider role in
group outcomes.
Educational qualifications and ethnicity
We now go on to explore the possibility that different levels of educational
qualifications have different salience for minority ethnic groups. That is, while
high qualifications might be a passport to success across groups, do low qualifications carry with them an additional penalty that would help us to understand the particular lack of upward social mobility experienced by the
Pakistanis in our sample?
Figure 3, which is based just on raw counts, gives some support to the
notion of differential impact for minority groups (but not white migrants)
with no qualifications. Relatively small proportions with no qualifications
from the Pakistani, Indian and Caribbean groups end up in the professional
or managerial classes compared to the white groups. The ethnic penalty does,
then, seem greater for the least advantaged; and to the extent that any
minority group is concentrated among those with no qualifications, they will
then suffer disproportionately. But for lower qualifications (no qualifications
plus level 1 qualifications), the differential impact persists only for the Pakistani group, rather than for all the minority groups illustrated.2 Caribbeans
and Indians, even with the rather minimal certification offered by having
level 1 qualifications, seem able to catch up with rates of entry into the
professional and managerial classes of the white majority. But we see that
only 10 per cent of Pakistanis and 14 per cent of Bangladeshis with lower
levels of qualifications end up in the professional or managerial classes. For
those with higher qualifications, there seems also to be some degree of differentiation, with the disadvantage experienced by Pakistanis and Bangladeshis also continuing at this level. Interestingly, though, here they are
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17
white migrant
29
65
8
Ethnic group
Pakistani
no qualifications
lower qualifications
higher qualifications
10
47
10
Indian
28
69
9
Caribbean
26
51
15
white non-migrant
29
64
0
10
20
30
40
50
60
70
80
%
Figure 3 Per cent in professional or managerial classes by ethnic group and
broad level of qualifications
Source: ONS Longitudinal Study, author’s analysis.
Notes: ‘lower’ refers to no or level 1 qualifications; ‘higher’ refers to level 1 or
2 qualifications.
joined by the Caribbeans who appear, therefore, better able to overcome the
ethnic penalty at lower compared to higher levels of qualifications. Thus, the
picture seems to be not simply that minority groups need qualifications to
succeed and are peculiarly disadvantaged at lower levels of qualifications.
Rather, qualifications, and different levels of qualifications, themselves
appear to be less salient for outcomes for those from certain minority ethnic
groups.
However, this does not take account of the other factors impacting on social
class outcomes that we have argued to be important, notably the parental
context experienced while these respondents were growing up. To explore the
relevance of different levels of qualifications in the multivariate context, therefore, a model was estimated which included interactions between ethnicity and
educational qualifications (controlling for the other variables in the models in
Table 2). Given the problems with meaningful interpretation of interaction
effects (Ai and Norton, 2003), predicted probabilities and their associated 95
per cent confidence intervals were calculated from the model at various levels
of class background and educational qualifications to illustrate the extent to
which these interactions made a difference. These probabilities are illustrated
in Figure 4 for lower qualifications and Figure 5 for higher qualifications. In
both figures, characteristics other than ethnic group are held constant. The
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Lucinda Platt
white non-migrant
-- with interaction
Ethnic Group
Caribbean
-- with interaction
Indian
-- with interaction
Pakistani
-- with interaction
white migrant
-- with interaction
.1
.2
.3
.4
.5
.6
Predicted probability prof/man class
Figure 4 Predicted probabilities from simple model and model with
ethnicity-education interaction, at lower educational qualifications
Source: ONS Longitudinal Study, author’s analysis.
Notes: other characteristics are male, partnered, in owner occupation, parents
without higher qualifications, working class background, low ethnic minority
density in ward where grew up, growing up in a household with one car, aged
8–11 in 1971. Standard errors have been adjusted for clustering on
individuals.
probabilities illustrated are those for being in the professional or managerial
classes based on, first, a model that includes a simple binary variable for lower
or higher qualifications and, second, a model which interacts this variable with
ethnic group. If educational qualifications had a similar impact across groups
then we would expect the predictions from the simple model and the interaction model to be similar at both lower and higher levels of qualifications.
Instead we see, in Figure 4, that both Indians and Pakistanis experience substantially and significantly lower probabilities of professional managerial
outcomes if they have lower educational qualifications according to the interaction model.
Figure 5 shows that while the interaction model results in higher (though
not statistically significantly different) probabilities at higher levels of
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white non-migrant
-- with interaction
Ethnic Group
Caribbean
-- with interaction
Indian
-- with interaction
Pakistani
-- with interaction
white migrant
-- with interaction
.5
.6
.7
.8
.9
Predicted probability prof/man class
Figure 5 Predicted probabilities from simple model and model with
ethnicity-education interaction, at higher educational qualifications
Source: ONS Longitudinal Study, author’s analysis.
Notes: as for Figure 4.
education for both the Indians and the Pakistanis compared to the simple
model, the net result for the two groups is very different. The probabilities for
Indians at higher levels of qualifications outstrip those for the white majority
suggesting a particular ability to capitalise on education (or the fact that their
qualifications are at the upper end of the broad level 3+ band), while Pakistanis still lag behind the white majority in terms of their chances of professional or managerial class outcomes even when they have passed this
threshold of qualifications.
What we see then, is some evidence for a differential ethnic penalty, with
greater obstacles at lower educational levels of qualifications. At the same
time, we also see a large specific penalty for the Pakistani group, which cannot
be fully accounted for by relative concentration at lower qualifications levels.
We return to discussion of these results in the conclusion. First however, we
turn to a consideration of models estimated separately for the minority groups
to ascertain if they can help us to understand further the different roles of both
class background and educational achievement.
© 2007 The Author. Journal compilation © 2007 The Editorial Board of The Sociological Review
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Lucinda Platt
The effects of education and background within groups
Table 4 illustrates the main results for models of professional or managerial
class outcome estimated separately for the five main ethnic groups. It shows
that for the minority groups, once we take account of the fact that background
may be channelled through educational achievement, parents’ class, educational and economic resources do not seem to play much of an additional role
in the success of their children. They do appear to have an important additional role for the white majority, as we would expect from the pooled model.3
There is some evidence for Indians that the economic resources indicated
by car ownership provide an additional advantage. However, for this group the
effects of housing tenure reverse the expected pattern, and are statistically
significant. Those Indians who grew up in both local authority housing and
private rented housing have increased chances of professional / managerial
class outcomes relative to those who grew up in owner occupied housing. This
supports the evidence that owner occupation is not such a clear indicator of
economic position or relative advantage for South Asian groups as it is for the
population as a whole (Phillips, 1997), especially given the way that owner
occupation may constrain opportunities for geographical mobility. It may also
suggest that there was some substitution by Indian parents in the 1970s and
1980s in relation to the allocation of resources between home ownership and
the promotion of the success of their children by other means.4
Education, as noted before, is the primary route to upward mobility, and
the coefficients for educational qualifications are large across the groups.
However, we see among Pakistanis that having level 1 qualifications does
not constitute an advantage over and above having no qualifications. It is
only at levels 2 and 3 that qualifications increase the prospects of professional or managerial class outcomes relative to those from the same ethnic
group with no qualifications. This is consistent with the raw differences in
outcome by educational qualifications illustrated in Figure 3, and indicates
that for this group obtaining any qualifications is not sufficient to improve
life chances. There seem to be additional obstacles which mean that the
effect of qualifications only become relevant at higher levels. This is also
suggested by the apparently smaller coefficients for the effects of education
on social class outcomes for this group compared to the other groups.
However, cross-equation tests of the coefficients could not reject the possibility that they were same as those for the white non-migrant group (perhaps
as a result of the relatively small sample size for the Pakistani group).
Such comparability was also the case for the coefficients for the Caribbean
and white migrant groups. By contrast, the education coefficients for the
Indian group were all found to be significantly different from those for the
white non-migrant group. Those of Indian ethnicity therefore not only get
more qualified than the other groups – including by channelling background
advantage into education, but seem particularly able to capitalise on their
qualifications at all levels in relation to social class outcomes.
502
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© 2007 The Author. Journal compilation © 2007 The Editorial Board of The Sociological Review
-0.186 (0.179)
0.066 (0.265)
0.178
0.445
-2.521
217
Tenure at origin (base is owner occupation)
Local authority
-0.298 (0.018)***
Private rented
-0.193 (0.025)***
Car ownership at origin (base = none)
1 car
0.180
2 or more cars
0.292
Constant
-2.427
LR chi2 (df)
31,402
115,478
Source: ONS Longitudinal Study, author’s analysis.
Notes: as for Table 2.
N
0.169 (0.241)
0.400 (0.372)
Father’s qualifications (base = none)
No co-resident father
0.166 (0.034)***
Father with qualifications
0.233 (0.025)***
1,010
(0.170)
(0.329)
(0.454)***
(24)***
0.142 (0.402)
0.281 (0.263)
Mother’s qualifications (base = none)
No co-resident mother
-0.101 (0.054)
Mother with qualifications
0.116 (0.029)***
(0.018)***
(0.025)***
(0.035)***
(24)***
-0.322 (0.233)
-0.373 (0.264)
0.176 (0.277)
1.796 (0.435)***
2.102 (0.437)***
3.137 (0.432)***
Caribbean
0.336 (0.020)***
0.015 (0.019)
-0.128 (0.041)**
Origin class (base = working)
Service class
Intermediate
Other
Study member’s qualifications (base = 0)
Lower (level 1)
1.009 (0.028)***
Middle (level 2)
1.451 (0.028)***
Further (level 3+)
2.752 (0.030)***
White non-migrant
0.334
0.313
-3.035
419
1,287
(0.156)*
(0.275)
(0.435)***.
(24)***
1.051 (0.276)***
0.550 (0.276)*
0.064 (0.427)
-0.044 (0.268)
-0.206 (0.469)
0.508 (0.425)
0.190 (0.262)
-0.212 (0.230)
0.153 (0.344)
1.904 (0.372)***
2.348 (0.373)***
4.124 (0.377)***
Indian
0.255
0.314
-3.027
150
557
(0.255)
(0.559)
(0.599)***
(24)***
0.239 (0.404)
0.548 (0.485)
1.222 (0.648)
-0.083 (0.504)
0.059 (0.649)
0.070 (0.836)
0.085 (0.463)
-0.246 (0.308)
-1.321 (0.467)**
0.536 (0.456)
1.465 (0.444)***
2.971 (0.399)***
Pakistani
(0.103)
(0.170)
(0.203)***
(24)***
2,893
-0.005
0.189
-2.234
793
-0.216 (0.111)*
0.024 (0.140)
-0.153 (0.145)
0.082 (0.176)
-0.217 (0.222)
0.106 (0.170)
0.111 (0.143)
0.169 (0.129)
0.070 (0.169)
0.805 (0.175)***
1.680 (0.175)***
2.777 (0.178)***
White migrant
Table 4 Logistic regressions of probability of professional/managerial destination in 2001, controlling for background and
individual characteristics, by ethnic group
Making education count
503
Lucinda Platt
Discussion
So how do we understand these results and what are their implications? The
situation in which children grew up, including their parents’ characteristics and
social class, cannot on its own account for differences in the outcomes of those
from different ethnic groups. Indeed while background remains an important
determinant of success for the white group, the minority groups predominantly represent a much more ‘meritocratic’ profile than the majority, with
educational qualifications being the determinants of success. For certain
groups (Indians), any existing background advantage is channelled through
education, while for others (Caribbeans) background appears simply irrelevant. But for all minority groups, attainment of qualifications is disproportionate to that expected from class background when compared with the white
majority; and for all minority groups bar the Pakistanis and Bangladeshis,
education provides the means to higher rates of upward mobility than those
from the white majority from the same cohort and with the same background
achieve. The findings suggest both common processes across minority groups
– attention to achieving qualifications, and relative disadvantage for those with
no qualifications compared to the majority – as well as distinct patterns which
seem to be group-specific. Bringing the various results together, we can characterise these distinctive patterns and consider their implications for the meritocratic ideal – if ideal it is (Aldridge, 2001) – in the following way.
The patterns for the white non-migrant majority refute the notion that
Britain is a meritocracy. Class and other aspects of background continue to be
relevant to individuals’ social class outcomes. And this is the case even after
own educational qualifications are taken account of.These findings of a society
stratified by class set the context for the experience of other groups.
White migrants would appear to represent the immigrant success story.
They achieve upward mobility from predominantly working class backgrounds via means of educational qualifications. At the same time they reflect
or absorb existing patterns of stratification insofar as those from more privileged backgrounds do better than those from working class backgrounds, by
acquiring equivalently higher rates of qualifications. Thus, employing the language of narratives of assimilation (Portes and Zhou, 1993), they ‘assimilate’
both in relation to outcomes and in relation to social processes.
The Caribbean picture is one of within-group meritocracy in a nonmeritocratic context. Education brings returns that are roughly commensurate
with those for the population as a whole; and, except for those with no
qualifications, these returns are not significantly enhanced or reduced at
higher or lower levels for this group. Class background brings no advantage,
even before education is considered. However, this suggests that the expected
returns from class advantage, such as networks and contacts, are unavailable to
this group. The relatively geographically dispersed and socially integrated
profile of this group (Peach, 2005), may have costs in relation to limited ability
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to draw on forms of ‘ethnic capital’ at the same time as exclusion from
majority-dominated elite networks and resources is enforced through discrimination. Retaining advantage across generations, which is often seen as
central to higher class position and the strategies of its incumbents (Bourdieu,
1997) does not appear an option for this group, which therefore places them at
a relative disadvantage. Moreover, educational qualifications may bring relative success, but do not protect from higher risks of ‘failure’ in terms of higher
unemployment rates (Platt, 2005b).
The Indian group makes effective use of qualifications to advance, getting
particularly high returns for higher levels of qualifications. We may see evidence here for a positive role of ‘ethnic capital’ in, terms of group as well as
individual resources, fostered by selected residential proximity (Dorsett,
1998), in securing a well-qualified second generation. Background resources,
to the extent that they make themselves felt, also do so through education,
thereby reinforcing a culture of attainment, and meaning that small section
which grew up in a service class family has particularly high probabilities of
professional or managerial class outcomes. The Pakistanis share with the
Indians a clearer differentiation in chances at lower and higher levels of
qualifications than the majority. However, in this case, it does not compensate
for an overall high penalty in relation to social class outcomes. Class background does not constitute a strong influence on outcomes for this group,
either positively or negatively, once educational level is taken into account.
Thus this group would appear to experience both general ethnic penalties
associated with lack of salience of class background and disadvantage at low
levels of qualifications alongside a specific penalty which renders this group
peculiarly disadvantaged. Group resources (or ‘ethnic capital’) would not
appear to offer great potential in relation to upward mobility – though they
may provide other benefits, such as protection against racism.
The increasing levels of qualifications in this group may mean that, over
time, the nature of group resources changes. Moreover, different patterns of
migration for this group, with a greater proportion of the sample being found
in the 1981 cohort compared to the 1971 cohort, may mean that their experience may become closer to that of the other minority groups over time. On the
other hand, there appears to be the danger that in the face of both a class
stratified and discriminatory society, those who are vulnerable on both these
fronts will suffer disproportionately.
On the one hand, the effective utilisation of educational qualifications
among minorities bodes well for a future which is likely to continue to emphasise the importance of certification. On the other hand, lack of equality of
opportunity, can compound the effect of the lack of equal starting positions
that vex the whole meritocratic ideal, as demonstrated so effectively in
Young’s (1958) original representation of a ‘meritocracy’.
Received 7 March 2006
Finally accepted 1 December 2006
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Lucinda Platt
Acknowledgements
The permission of the Office for National Statistics to use the Longitudinal Study is gratefully
acknowledged, as is the help provided by staff of the Centre for Longitudinal Study Information
& User Support (CeLSIUS), especially Julian Buxton. The clearance number for this paper is
4004a. The author however, retains full responsibility for the interpretation of the data. Census
output is Crown copyright and is reproduced with the permission of the Controller of HMSO and
the Queen’s Printer for Scotland. I am grateful for comments from seminar participants in
Göteborg, and for the detailed and valuable feedback from two anonymous referees, as a result of
which this paper was completely reworked.
Notes
1 Distributions are not given for all minority groups to avoid risk of disclosure.
2 A similar pattern is observed for the Bangladeshi group, though these estimates have not been
illustrated due to the small cell sizes on which they are based.
3 Though we should be alert to the fact that with a sample size of 115,000 many coefficients will
achieve statistical significance at conventional levels as a result.
4 Though, owner occupation being such a dominant tenure among Indians, we should also
recognise that those in rented accommodation are likely to be a quite specific sub-group.
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