Download Ethnic differences in school-to-work transition in England and Wales

University of Bristol
Department of Sociology
The impact of residential segregation and ethno-religious
background on transition from school to work in Britain1
Nabil Khattab Ibrahim Sirkeci
University of Bristol
European Business
School London
Tariq Modood
Ron Johnston
University of Bristol
University of Bristol
20 July 2006
Bristol
1
Acknowledgement
The project was funded by the Leverhulme Trust as part of the Leverhulme Programme on Migration
and Citizenship in the Centre for the Study of Ethnicity and Citizenship at University of Bristol. The
support of the Office of National Statistics, CCSR and ESRC/JISC Census of Population Programme is
gratefully acknowledged. The authors alone are responsible 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.
1
The impact of residential segregation and ethno-religious background on transition from
school to work in Britain
Nabil Khattab, Ibrahim Sirkeci, Tariq Modood, and Ron Johnston
Abstract
It is widely accepted that education is the most important factor in determining one's social
class and chances of upward mobility in modern societies. In Britain and other western
countries, the main channel through which education shapes one's social mobility chances is
the labour market. The empirical evidence from Britain suggests that people from Indian and
Chinese backgrounds are doing as well, if not better than, the white majority in many aspects,
if not better, whereas people from Caribbean, Pakistani and Bangladeshi backgrounds have
lower levels of achievement in terms of education and employment than the white majority. In
previous studies on ethnicity and transition from school to work, however, it has been pointed
out that non-white minorities are likely to face ethnic penalties while entering the labour
market. In this paper, drawing on data obtained from the 2001 UK census (CAMS data), we
carry out a new multilevel analysis to explore the influence of the ethno-religious background
on the one hand and neighbourhood-based factors such as ethnic segregation and levels of
deprivations on the other hand on the transition from school to work in England and Wales. In
addition, we introduce a new way to operationalise the transition from school to work by
measuring the distance between formal qualification and actual skill level required by the
current job. The scale indicates the returns to education on a 9-point scale with -4 indicating
the lowest returns and +4 indicating the highest returns. The results suggest that the ethnic
differences in Britain are more complex than has been previously thought, with colour and
culture playing important roles in shaping these differences. The results also suggest that
segregation is negatively associated with returns to education among minorities indicating that
segregation in the UK might be taking the form of the black American ghetto among some
groups. These and other findings are discussed in the paper.
2
The impact of residential segregation and ethno-religious background on transition from
school to work in Britain: A multilevel model
Nabil Khattab, Ibrahim Sirkeci, Tariq Modood and Ron Johnston
Introduction
In previous studies it has been argued that ethnic and religious minorities in Britain follow a
different pattern in their educational and occupational endeavours than their white British
fellow citizens. Some minority groups were over represented in higher education while some
others were over represented among unemployed or inactive populations, and some
overrepresented in both. In this study, we have analysed some key factors in order to explain
the school-to-work transition of minority groups using 2001 UK Census individual data.
The analysis has focused on age, gender, ethnicity, religion, and type of family, marital status,
country of birth, ownership and type of accommodation, educational qualification, and
occupational level. We have contrasted these individual level characteristics with the levels of
deprivation and isolation of minority groups at a neighbourhood level. For analytical purposes,
we have formulated school-to-work transition in terms of returns to educational qualification
which is measured by level differences between the level of qualification and level of
occupation. To overcome the shortcomings of earlier studies, we have also introduced an
interaction variable, ethno-religious identity reflecting ethnic and religious affiliation.
These three, the ethno religious variable, transition formulation, and multilevel analysis have
proven to be innovative and pioneering and provided us with a rich array of findings shedding
light on ethnic and religious diversity and its manifestations in UK employment patterns.
Muslim segments of different ethnic groups are found more vulnerable and disadvantaged,
suffering a relative deprivation, which is responsive to where they live and with whom they are
more likely to be in contact. Differences have also been related to age, gender, and tenure of
accommodation, marital status, and family types.
It is widely recognised that education is the most important factor in determining one’s
occupational class and chances of upward mobility in modern societies. The process through
which education shapes one’s social mobility chances is largely known as transition from
school to work. This process occurs when people enter the labour market seeking to convert
3
their human capital resources into economic and material rewards. In previous studies on
ethnicity and transition from school to work, it has been pointed out that minorities are likely to
face ethnic penalties while entering the labour market (Heath and McMahon 1997; Heath and
McMahon 2005). These penalties can take various forms such as longer period of
unemployment until first full time job than amongst the majority ethnic group, lower wages for
the same job and qualification, underemployment and job allocation (Craig, et al. 2005; Heath
and McMahon 1997; Modood, et al. 1997). Although all ethnic minorities face discrimination
in the labour market (Craig, et al. 2005; Khattab 2005 ), in recent years it has become clear that
some minorities face larger and more complex penalties than others. For example, Modood
(2005) has argued Asian Muslim minorities (Pakistanis, Bangladeshis and perhaps Muslim
Indians) suffer a distinctive and complex kind of racism comprising both of a colour-racism
and a cultural-racism. This may even be more evident for Muslim women in the labour market
who are likely to face an extra gender penalty in addition to their different skin colour and
culture, especially those women who wear the headscarf, the Hijab as some recent studies
suggest (Ahmad, et al. 2003; Dale 2002; Dale, et al. 2002). Other recent studies have pointed
out that the disadvantaged position of some minority groups may have stemmed from their
spatial concentration in highly deprived areas (Fieldhouse and Tranmer 2001; Peach 2005;
Peach 2006; Smith, et al. forthcoming; Virdee 2006). Until recently sociologists in the UK
could not study the impact of spatial segregation and the ethno-religious background of
individuals at the same time due to the lack of information on religion in previous censuses and
the inability to access datasets with both individual and spatial information. The 2001 UK
census and the availability of the 2001 Individual Controlled Access Microdata Sample
(CAMS) made it possible to study the impact of spatial segregation and deprivation levels at
the neighbourhood level and other individual factors including ethnicity and religion. This new
investigation allows us to empirically examine whether the ethno-religious differences are
accounted for once we control the concentration and deprivation levels within the
neighbourhood.
Religion and ethnicity
The discourse on minorities (immigrants) has shifted quite significantly since the 1960s. We
can identify 3 main phases from the 1960s to 1990s and one more recent development (2000s)
in relation to this discourse. According to Peach (2006: 631), this discourse has shifted from
‘colour’ in the 1960s to ‘race’ in the 1970s and 80s to ‘ethnicity’ in the 1990s. Over the past
decade there were number of national and international events that have brought religion under
the spotlight including 9/11, the war on terror, the ethnic disturbances in the northern cities in
4
the UK and 7/7. These events on the one hand and the availability of information on religion in
the 2001 UK census and some other important datasets such as the 4th National Survey of
Ethnic Minorities (Modood, et al. 1997) has placed religion in the centre of the discourse on
minority populations (Peach, 2006: 631). We probably do not want to move from ethnicity to
religion so quickly, if at all. True, religion seems to be an important factor (Brown 2000).
Drawing on secondary analysis of the Fourth National Survey of Ethnic minorities, Brown
(2000: 1058-1059) confirmed that religious groups are different from each other in their socioeconomic profile, with Muslims being the most disadvantaged group. This latter point has been
confirmed by other recent studies using census data such as Peach (2006) and Smith et al.
(forthcoming). However, Brown (2000) went on to challenge this latter point by referring to
the relative advantaged Indian Muslim minority and that the latter is significantly different
from the other two major Muslim groups of Pakistanis and Bangladeshis. This suggests that
while we may benefit from controlling for religion, it may be even more beneficial to do so in
conjunction with ethnicity as shown by Khattab (2005 ). In addition, by combining religion
with ethnicity we will be able to examine more complex arguments such as analyses of cultural
racism (Modood 2005). Modood argues that racialised groups who hold (and are perceived by
the majority to hold) a different culture and life-style will encounter an additional level of
discrimination and hostility:.
The hostility against the non-white minority is likely to be particularly sharp if the
minority is sufficiently numerous to reproduce itself as a community and has a
distinctive and cohesive value system that can be perceived as an alternative, and a
possible challenge, to the norm. (2005: 38)
By discussing racism, ethnicity and Muslims in Britain, Modood (2005) argues that all nonwhite minorities in Britain face racism (on the basis of colour or physical appearance), but
within these minorities, Muslims face more severe and more hostile racism because they are
less likely than others to adopt English ways. Thus, in this paper, following Khattab (2005) we
will use the information on religion and ethnicity to derive a new variable of ethno-religion
background, so that we can examine religious differences within ethnic groups and ethnic
differences within religious groups in relation to the transition from school to work in England
and Wales.
Residential segregation
5
Previous and recent studies have acknowledged the importance of space (place) in general and
residential segregation in particular in determining the social and economic conditions of
people (Fieldhouse 1999; Fieldhouse and Tranmer 2001; Peach 2005; Peach 2006). From the
physical and material point of view, some places can be well resourced, prosperous and
affluent, whereas some places can be worse-off. The material differences affect the amount of
opportunities available to their residents and the way these places are, symbolically, perceived
by people living in them. Equally important, these differences may also shape the views and
perceptions of others towards the residents of these places and attach different attributes and
different qualities to each group of people based on the area they come from (in the UK we
might use the example of the post code effect in applying to jobs). In the UK there is a
significant body of literature on residential patterns of ethnic and religious minorities. The
evidence provided by this body of literature suggests that ethnic minorities in the UK are
moderately segregated, with Bangladeshis and Pakistanis are the most segregated groups
(Johnston, et al. 2006; Johston, et al. 2004; Owen 2003; Peach 2005; Peach 2006; Smith, et al.
forthcoming). Segregation per se is not a problem and is not a source of concern because in
fact one of the most segregated groups is the Jewish community and no one yet has suggested
that the Jewish segregation is problematic in any way. On the contrary, according to some
recent studies focusing on ethnicity and religion, the Jewish community appeared to have the
most desirable social and economic profile (Khattab 2005 ). However, the problem that is
pointed out by previous studies in relation to ethnic residential segregation is that some groups
are disproportionately concentrated in deprived areas, and in particularly Muslims (Peach
2006; Smith, et al. forthcoming). Nevertheless, while some Asian Muslims are segregated and
concentrated in areas of multiple deprivation, London presents an exceptional example where it
has been found that Muslims are residentially well mixed (Peach, 2006: 652). But this is
because the segregation mostly occurs along ethnic lines (Smith, et al. forthcoming), and
therefore analysing segregation by religious groups only shows that Muslims in London are not
segregated. This is another reason why we ought to use an ethno-religious classification rather
than investigating ethnicity and religion separately.
However, it is worth mentioning that it is geographers and demographers not sociologists that
have carried out most of the studies on residential segregation in the UK. Not surprisingly then
that these studies have paid attention mostly to the levels of segregation and size of groups
within segregated areas, but failed to consider number of important factors and the relationship
between these factors such as the social and community based-networks, the structure and type
of these networks and the level of organisation within these segregated communities.
Moreover, one of the factors that has been rarely studied in the UK in relation to residential
6
segregation is whether there is the scale of economic and social activities within these
segregated communities, and what level and form they take. In relation to these latter points,
the literature in the US on segregation is much more developed than in the UK. For example,
Massey (2002) differentiates between two process of segregation, voluntary and involuntary
segregation. He argues that “high levels of involuntary segregation undermines the socioeconomic well-being of minority groups by subjecting them to uniquely disadvantaged
neighborhood environments brought about by the concentration of poverty and its correlates.”
(Massey, 2002: 351). Whether the process of segregation has occurred involuntarily or not is
important for the understanding of the social and economic conditions of the segregated
communities, yet there is one more condition that needs to be accounted for which is the kind
of social and economic capital (resources) available within the segregated communities.
Segregation, and in particular involuntary segregation, becomes a real problem if it occurs to
communities lacking resources and resident in areas of multiple deprivation. Therefore we
should not immediately conclude that all forms of forced segregation might undermine the
socio-economic well-being of minority groups. The Venetian Jewish ghetto established in 1516
(the ghetto nuovo) is a good example. The ghetto was a form of an involuntary segregation of
the Jewish community, yet there were very developed social and economic activities within the
ghetto (Bernasconi 2002). By forcing the Jews to move into the new ghetto, “Christians could
enjoy the commercial benefits that the Jews provided without having to live alongside them”
(Bernasconi 2002: 341). When Jews migrated in large numbers from Europe (Germany) in the
second half of the 19th century to America, the term ghetto also migrated with them and was
used to refer to distinct Jewish districts within the major cities such as the Great Ghetto of
Manhattan and the Jewish ghetto in Philadelphia. The use of the term has then been extended
to apply to other immigrant groups including Africans American (Bernasconi 2002). The
Jewish ghettos in America were not as involuntary as the Venetian Jewish ghetto, whereas the
black ghettos were an example of forced segregation (Bernasconi 2002), and an example of
areas of multiple deprivations, crime, unemployment, and various social problems.
To differentiate between the segregation of Jews for example and that of blacks so that we can
understand why the former did not undermine the socio-economic well-being of Jews, whereas
the latter is associated with the failure of blacks, we will use the analysis of Zhou (2005) and
Portes and Manning (2001) of ethnic economic enclaves. In their analysis of immigrant
enclaves and why some immigrants have achieved economic success despite their segregation,
Portes and Manning (2001) use the example of Jews in Manhattan and the Japanese on the
west coast. In relation to Jews they have argued that “Jewish enclave capitalism depended, for
its emergence and development, precisely on those resources made available by a solidaristic
7
ethnic community: protected access to labor and markets, informal source of credit, and
business information.” (Portes and Manning 2001: 572). The discussion of the Jewish and
Japanese enclaves has lead them to conclude that there were three prerequisites needed for the
emergence of an ethnic enclave economy: the presence of a substantial number of immigrants
with business experience acquired in the sending country; the availability of source of capital
and the availability of source of labor” (Portes and Manning 2001: 574).
Zhou (2005), in her discussion of ethnicity and social capital, makes a clear distinction
between ghetto as an area of multiple deprivations and the ethnic enclave economy as an area
characterised with developed social capital and economic resources. She refers to the black
ghetto and the China Town to discuss how and why the former is poor in social capital and
economic resources and the latter is rich and fairly developed. Her analysis and the analysis of
Portes and Manning, show that although the size of the minority and the level of segregation
are indeed important factors, one should look at the social networks and economic activity that
take place within the segregated areas. The focus should be on the resources (social and
economic) available within these areas including the supply of labour and the buying capacity
of ethnic clients (Portes and Manning 2001).
As mentioned earlier, in the UK very few sociologists have looked at residential segregation
and its impact on the labour market attainment of ethnic minorities. One study, however, found
that “high unemployment rates among ethnic minorities in general were a major factor behind
the high unemployment rates in areas where they were concentrated” (Modood et al 1997:
.191), and while the level of analysis was unable to establish a relationship between
segregation and unemployment, it suggested that the relationship between unemployment rates
and concentration of ethnic minorities is complex and would merit further exploration. Indeed,
exploring the issue of segregation and labour market outcomes among ethnic minority requires
a different and more advanced methods of analysis such as multilevel analysis in order to
capture the complex relationship between them as in some of the studies carried out by sociogeographers (Fieldhouse and Tranmer 2001). These methods have been recently used by
sociologists to study unemployment and segregation in other countries such as the US and
Israel (Khattab 2006; Mouw 2000). Thus, in this paper we use a multilevel approach to analyse
the transition from school to work among minority groups. This approach will allow us to
control for both individual factors such as the ethno-religious affiliation as well as
neighbourhood factors such as segregation.
Transition form school-to-work
8
By definition, transition from school to work reflects some kind of process of entering the
labour market having completed education or left school at earlier stage. It is a process through
which people seek to match their acquired qualifications to suitable employment. In the
literature on transition from school to work, we mainly found a reference to young people
moving into the labour market (Ainley, et al. 1997; Halpern 1985; Nielsen, et al. 2003). For
example, Ainley, et al (1997) has defined school-to-work transition as “the period during
which young people move from the principal activity being full-time schooling or its
equivalent to that in which their principal activity is work” (1997: 12). Nowadays, however,
the process of moving into the labour market has dramatically shifted from being a one step
linear, straightforward and smooth track with no major breaks to a more complex, fragmented
and individualised process depending on the navigational and negotiating abilities of young
people (Goodwin and O’Conner 2005). Because of the increasing importance of educational
attainment and qualification in getting well-paid jobs, young people who leave schools without
high levels of qualifications are likely to experience longer periods of unemployment and are
likely to end up in low paid jobs. It is evident that this process is ever more fluid reflecting
labour market changes and difficulties in entry, forcing young people to remain in education
longer and to return to education after some work experience because employers seek more
experienced and qualified candidates (Smyth, et al. 2002).
The most traditional method to study the transition from school to work is by examining
the relationship between the highest qualification and occupational classes and the impact of
the former on the latter (Cheung and Heath in press; Heath and McMahon 1997; Heath and
McMahon 2005; Shavit, et al. 1998). Heath and McMahon (1997; 1999; 2005) have stated that
educational qualifications plays a key role in determining one’s class position. The empirical
evidence suggest that occupational attainment of individuals is influenced by the amount and
kind of education acquired by people, yet the influence of educational attainment on
occupational trajectories may vary in different countries (Shavit, et al. 1998), and between
different minority groups within each country (Cheung and Heath in press). In previous
studies, scholars have addressed various aspects and factors affecting the relationship between
education and employment including looking at institutional or structural effects, certifications
and policies (Turner 1960; Kerckhoff 1995; Robert 2004); role of education certificates (Heath
and McMahon 2005); generational differences (Platt 2005a); educational disadvantage and
different minority behaviours (see Modood 2004, Berthoud et al., 2000, Gillborn & Mirza,
2000, Owen et al., 2000, Richardson & Wood, 1999); effects of selectivity of international
migration (Munz, 2004, Phalet, 2003, SOPEMI, 2000); influences on the income levels (Kalter
9
and Kogan 2003); role of racial and/or ethnic discrimination (Peach 2004, Anthias and YuvalDavis, 1992, Wrench and Solomos, 1993).
However, in our view, the transition form school to work is a process through which
people try to get the optimal match between their qualification and labour market position or
occupation. Some people may attain a job that does not match their qualification, and thus they
may be classified as over-qualified if their qualification is higher than what is required for the
job or under-qualified if the reverse. While the former can be seen as disadvantaged and have
relatively low returns to their qualification, surely the latter are very advantaged with much
higher returns than what they would normally receive should their qualifications and jobs have
matched. The literature on transition from school to work or more specifically on the
relationship between education and employment does not pay sufficient attention to the
measuring and studying the gap or distance between level of qualification and the skill level
required by the job. In our view, examining the distance between the actual qualification and
the skill level required for each occupation is a better and more sensitive way to measure the
transition from school to work for the following reasons:
1) Examining the distance between qualification and job skill level would allow us to
explore a wider range of transitions such as various levels of mismatches, both positive
(such as underqualified people) and negative mismatches (such as overqualified
people).
2) This method allows us to not only explore the simple association between qualification
and jobs, but more importantly how the relationship between the two factors is shaped
by other independent factors such as residential segregation and deprivation levels at
the area or locality level and the ethno-religious background at the individual level.
3) The method we use takes into account three variables, educational qualification,
occupational class and economic activity. By taking economic activity into account we
include unemployed people who receive no returns to their qualifications during the
period of unemployment. By doing that, we try to capture some of the dynamics of the
transition process and in particular a more adequate representation of people entering
the labour market, including young adults.
Methodology
The analysis here is based on the Controlled Access Microdata Sample (CAMS), which is a
more detailed version of the licensed SAR file (Samples of Anonymised Records). The dataset
contains details on geography down to LA level as well as full details on occupations and
10
industry including other variables such as country of birth. The SARS data is a 3%
representative sample of the entire population of England and Wales. Our final sub-sample
includes about 250,000 individuals of working age (16 and 64 for men and 16 to 59 for
women).
Variables
School to work transition (dependent variable)
To assess the process, we have combined three variables from the Census data: a) level
of highest qualification, b) level of occupation is derived from the ISCO 88 (International
Standard Classification of Occupations) variable, c) economic activity (in previous week).
(Please see appendix 1).
The Census data provides 10 different occupation levels. We have regrouped them into five
skill levels according to ISCO 88 required skill levels for each category (for details see
Hoffmann and Scott, 1993). In doing so, we have excluded the categories of others, armed
forces and unknowns because these do not indicate skill levels, but have included “legislators,
senior officers and managers” into skill level 4 assuming that this group share a similar level of
returns to education with “professionals”. We also integrated the categories of unemployed and
inactive from the variable of economic activity in the previous week. Assuming that
individuals do not require skills to be unemployed or inactive, these appear in final
occupational skill levels as the fifth (“0 level”) category.
Then, applying a simple formula (see below) the school-to-work transition has been
transformed into scores ranging from –4 to +4 indicating the distances between levels of
qualification and occupation levels. In both, qualification and occupation, we reduced the
sample by eliminating unknown categories. For example, ‘armed forces’ in ISCO and ‘level
unknown qualifications’ in highest level of qualification were excluded. Then we obtained two
variables; qualification level ranging 0 to 4/5 and occupational level ranging from 0 to 4. In the
former 0 represents those with no qualification while it is unemployed or inactive in the latter.
By subtracting level of qualification from level of occupation we obtained a score, which we
define as transition score representing the level of match or mismatch between the two. This
score, we believe, gives an indication of returns to education in the labour market. Table 1
presents a summary of these scores by ethno-religious groups.
Transition score = level of occupation – level of qualification
(1)
(returns to education)
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In the analysis we have merged –4 into –3 and +4 into +3 due to few cases in these categories
and to reduce the number of categories for the sake of simplicity. Additionally, we have
recoded the variable further to create a series of dummy variables in order to run the multilevel
models. The latter recoding has to be done due to software restrictions, as full multinomial
models could not run using the MLWin software. In each model we examine the log-odds of
being in a certain transition outcome against the log-odds of being in the match outcome. The
full equation of the model is included in appendix II and the full range of models is presented
in appendix III.
Independent variables
Level-1
Gender: coded as 0 for men and 1 for women.
Place of birth: a dummy variable coded as 0 for UK born and 1 for overseas
Marital status: coded as 0 for unmarried and 1 for married. The unmarried category includes all
people who are not married at the time of the census (such as single, divorced and so on).
Ethno-religious background: this variable was derived using the two variables on ethnicity and
religion. We have created 14 different ethno-religious groups as follows:
Christians White-British (CWB), Muslim Indians (MI), Muslim Pakistanis (MP), Muslim
Bangladeshis (MB), Muslim others (MO), Jews White-British (JWB), Hindu Indians (HI), Sikh
Indians (SI), Chinese (Chinese), No religion (NR), Christians White-Irish (CWI), Christians
Black-Caribbean (CBC), Christians Black-Africans (CBA), Other White-British (OW) and
Others (O).
Age: indicates the respondents age and runs from 16 to 46 for men and 16 to 59 for women.
Level-2
In order to examine the influence of segregation and deprivation, we have linked the CAMS
dataset to a special dataset containing the relevant information at the neighbourhood (tract)
level. By using a unique tract number, we were able to link each respondent in the CAMS
dataset to his or her tract (neighbourhood). There were 1,138 tracts created (by grouping) out
of 8,869 wards with population mean of 45,731 in each tract (for information on how these
tracts have been created please see: http://asp.ccsr.ac.uk/dwp/info/Constructing_tracts.pdf,
12
visited on 14 July 2006). Two variables have been defined at the tract or neighbourhood. The
first variables was the Index of Multiple Deprivation (IMD) as it has been measured and added
to the dataset by the ONS, and the second was the Modified Index of Isolation (MII) (Johnston,
et al. 2004). The IMD ranks the neighbourhoods from the most deprived to the most affluent.
High score of the index indicate high deprivation and low score represents low level of
deprivation.
For
more
information
on
the
scale
see:
http://odpm.gov.uk/pub/446/Indicesofdeprivation2004revisedPDF2198Kb_id1128446.pdf (last
visited 20 July 2006).
Findings
In this section we will present and discuss our results. First we will present, briefly however,
the distribution of occupations using the one digit ISCO-88 classification by ethno-religious
background. Then we will present the differences between the ethno-religious groups in terms
of their transition scores (or returns to education) and will follow-up by presenting the models
from the multi-level analysis. For the ethno-religious differences in terms of levels of returns to
education and for the results of the multi-level analysis we use separate figures and models for
men and women.
Figure 1 presents the occupational distribution by ethno-religious group using the one digit
ISCO-88 classification excluding the armed forces. The figure reveals that while most of the
groups have different patterns, CWB, CWI and NRWB share a similar one. For example, they
are almost evenly represented within the top two classes of legislators, senior officials and
managers and professionals (29%, 29% and 28% respectively). The group with the highest
representation within the top two classes is JWB. They are over-represented with 44% of them
holding positions within these two classes followed by HI and Chinese with 35% and 33% of
them respectively holding jobs that can be classified in these two classes. In contrast to these
groups, MB and CBC are under-represented within these top classes with 17% and 19% of
them respectively occupying positions within these classes. MP are too under-represented
within these top classes with slightly over a fifth (21%) placing them just below the CBA and
SI (23% and 24% respectively). Unlike the Muslim groups mentioned above (MB and MP) MI
are just below CWB with slightly over a quarter of them (27%) in positions within these two
top classes, placing them in a better position than the other two Muslim groups but also ahead
of SI (one of the other two Indian groups).
Figure 1 about here
13
In addition to the differences within these two top classes, we would like to highlight the
differences within the class of service workers and shop and market sales workers. It is worth
noting here that MB are extremely over-represented within this class with 41% of them are
employed within this class followed by Chinese with slightly less than a third (30%) holding
positions as service and shop workers. In both cases, this is due to the high percentage of
people working in food businesses and the take away style restaurants.
In Figures 2 and 3, we present the ethno-religious differences in levels of returns to
qualification (transition) for men and women. As we have mentioned earlier, the scale
measures the distance (difference) between the formal qualification and the actual skill level
required within the class in which the person’s job is located. For the sake of simplicity and for
us to highlight the most significant patterns, we have restricted the presentation to three levels
of returns only: highest, match and lowest (the full results are in appendix III). We begin with
figure 2, which presents the relevant results for men. The results confirm that JWB enjoy the
highest level of return followed by CWB, SI and CWI. In contrast to the former groups, there
are two groups who are under-represented within the highest level of return: CBA and MO.
Figure 2 about here
While the percentage of Chinese people within the highest level is similar to that of other
groups such as MI, MB, CBC, their percentage within the lowest level of return is higher than
any other group with about 22% of them receive the lowest level of return to their qualification
followed by CBA and MO with about 21% and 17% respectively. It seems that relative to all
the other groups, CWB are under-represented with only 4% of them receiving the lowest level
of return to their qualification. It is worth noting here that despite the JWB being overrepresented within the highest level of returns, they seem to be slightly over-represented within
the lowest level as well. This might be due to the pattern of economic activity amongst the
orthodox religious Jews who usually do not participate in the labour market. Yet, in general
this group has a solid economic profile.
With respect to the match level, where the formal qualification matches the skill level required
within the occupational class, it seems that in general around third of the people within most of
the groups fall within this category. The only exceptions here are HI and CWI who are overrepresented within the match level with about 40% of them having jobs that suit their
qualification.
14
Now we move on to report the ethno-religious differences in levels of return among women.
Like men, about third of women among most of the groups hold jobs with skill level that match
their qualification. This pattern exists amongst JWB, HI, SI, CWI, OWB and CWB. The only
exception here are the four Muslim groups and in particular MB, MP and MI who are
significantly over-represented within the match level with about half (49.8%) of MB women
having this level of return.
Unlike men, no group of women reach the threshold of 5% representation within the highest
level of return, which was also the case among 5 of the groups in relation to men. The group
with the highest percentage of members holding positions with the highest level of return are
Chinese women (3.8%) followed by JWB (3.7%), CWB 3.5%) and SI (3.5%). Three out of the
four Muslim groups (MP, MB and MO), along with CBA, are well below the former groups.
The equivalent percentage for CWI women is 2.6% slightly below that of MI and SI women
(2.8%). In terms of representation within the lowest level of returns, it is worth noting that
Chinese women have the higher percentage (22.8%) followed by CBA (18%) and MO
(15.8%). In contrast, CWB women seem to be in a better position with about 5% of them only
having the lowest level of return on their qualification. Chinese women therefore appear to be
the most polarised group, whereas the CBA women appear to be the most disadvantaged
group.
Figure 3 about here
These two figures suggest that discrepancies exist between different ethno-religious groups and
to a lesser extent within some of the groups in relation to men and women. The comparison
between the two figures reveals that in most of the groups, women experienced a lower return
on their qualification than men and are less likely than men to be at the opposite end of the
returns’ scale (the highest level). This suggests that different patterns exist among men and
women, which justifies fitting different models to them. In order for us to learn more about
these patterns and about the differences between the ethno-religious groups we ran a series of
multi-level models for men and women with levels of transition scores as the dependent
variable. These models are presented in Table 1.
15
In Table 1 we present two models for men (Models 1 and 2) and two for women (Models 3 and
4). The first model for each gender contrasts the log-odds of the lowest transition score with
the match level, whereas the second model presents the log-odds for the highest score versus
the match position. The first predictor in Model 1 is place of birth indicating that the impact of
being born overseas vis-à-vis UK is not statistically significant. Unlike the former, being
unmarried has a significant negative impact on falling within the lowest level of returns. It
increases the log-odds of being in the lowest score rather than in the match level. Age seems to
increase the log-odds of being in the lowest level of returns, though we would expect the
impact of age to be in the opposite direction due to experience that is increases with age and on
the job training.
Table 1 about here
In terms of the impact of the ethno-religious background, Table 1 reveals that all the four
Muslim groups, HI, SI, CBC, CBA, OW and O are more likely to be represented in the lowest
level than in the match relative to CWB. The other ethno-religious groups including JWB,
Chinese, NR and CWI are not statistically different from the majority group (CWB). These
latter groups are as likely as CWB to experience the lowest level of returns.
The bottom part of the table shows the relevant results of the level-2 predictors. It appears that
the higher the deprivation level (higher scores of IMD) is, the more likely that people
experience the lowest transition score relative to the match score. In other words, a one unit
increase in the IMD score, while all other factors are assumed constant, would increase the logodds of the lowest transition score by (0.013). Like the impact of IMD, segregation (measured
by the Modified Index of Isolation – MII) operates in the expected direction and seems to
increase the log-odds of the lowest transition score, though the coefficient fails to reach the 5%
level of significance.
The second model for men contrasts the log-odds for the highest transition score with the
match (0) score. Being born overseas has a significant negative impact on the log-odds of
achieving the highest transition score (-0.38). Unmarried people are significantly less likely
than married people to be in the highest score of transition. Age, expectedly, increases the logodds of the highest score, as people become more experienced with time, many may be
promoted due to seniority and experience rather than on the basis of formal qualification.
16
Unlike the first model, here most of the main ethno-religious groups are not significantly
different from CWB. The only exceptions here are Chinese (0.60), CBA (-1.40), OW (-0.16)
and O (-0.44). While 3 out of the former four groups are less likely to be represented within the
highest score, Chinese men are the only minority to be significantly more likely to receive the
highest level of returns than CWB. If we take into account that in terms of the lowest scores
Chinese were not different from CWB, as we saw in the first model, then the conclusion that
Chinese men are a successful minority is inevitable.
The two level-2 predictors are both statistically significant and while the MII operates in the
expected direction, the IMD unexpectedly is positively associated with the highest score of
transition. This suggests that people in more deprived areas (high IMD scores) are more likely
to receive the highest level of returns. This might happen in deprived areas where high
qualifications are so limited, so that some people may be able to receive the highest level of
returns by disproportionately benefiting from relatively high qualifications. This latter point
will be discussed further in the next section. Unlike the impact of the IMD, the influence of
segregation (MII) is more consistent as it is negatively associated with the highest score of
transition suggesting that people living in highly segregated areas (high concentration of
minorities) are less likely to receive the highest score of transition. This suggests that in order
for people to receive higher returns to their qualification they probably would benefit from
living in mixed areas or in areas with low concentration of minorities. Nonetheless, we will
need further evidence before any solid conclusions can be made. Now we will move on to
present the comparable models for women so that we can see whether the same patterns
emerge for women or not.
Like in first model for men, here too (Model 3) the impact of place of birth and marital status
are significant and in the expected direction. Moreover, age is also significant and unlike in the
men model, it is in the expected direction. The impact of age for women suggests that old
women are less likely to experience the lowest score of transition than young women.
The pattern emerging from Model 3 in relation to the impact of the ethno-religious background
is only slightly different from the comparable pattern for men (Model 1). JWB and CWI
women are as likely as CWB to experience the lowest transition score. Unlike these two groups
of minority women, all other ethno-religious groups, except MI, are significantly more likely
17
than CWB women to face the lowest transition score. Surprisingly, MI women are less likely
than CWB to end up with the lowest transition score, yet the coefficient fails to reach the 5%
significance level. This means that MI women are not different from the other 3 groups (CWB,
JWB and CWI) in terms of their return transition score. It might be argued that the number of
MI women in the labour market is relatively small, and they do not join the labour market
unless they find suitable jobs. This explanation is also true for the other Muslim women,
though they seem to face lower returns than CWB suggesting that the explanation lies
elsewhere. We will re-visit to this point in the next section.
Model 3 shows that only one out of the two predictors measured at the neighbourhood level is
significant, and this predictor is MII. The coefficient (0.81) suggests that segregation increases
the log-odds of the lowest transition score for women. In other words, minority women
residing areas with high ethnic concentration are more likely to face the lowest transition score
than in areas with low levels of ethnic concentration.
In Model 4 we present the log-odds for the highest level vs. the match level among women.
The impact of place of birth operates in the expected direction and suggests that being born
overseas would reduce the log-odds of the highest score of transition. Marital status is
statistically insignificant, whereas the impact of age is similar to the comparable effect in
Model 2 for men. In terms of the impact of the ethno-religious background, perhaps the most
interesting and surprising result is that of MB women who are significantly more likely to
attain the highest transition score than CWB women. Because the Bangladeshi community is
highly concentrated in few places and over a quarter of the community in the UK lives in
London, we suspect that this result might be due to their residential pattern and because of the
very few women participating in the labour market. This is an important issue and worthy
discussing further in the next section of the paper. Like the former, Chinese women are too
more likely to attain the highest level of returns than CWB women. CBA and NR women are
the only two groups to have a significant negative coefficient indicating that these two groups
are less likely to receive the highest transition score than CWB women. The other ethnoreligious groups are not significantly different from CWB women.
The level-2 predictors are both significant. The relevant result for the IMD is similar to the
comparable result in Model 2 signifying that women in areas of high levels of multiple
deprivations are more likely to attain the highest transition score relative to the match score
18
than women living in low deprived areas. The impact of segregation is negative and it is in line
with the comparable impact found among men in Model 2. This means that both minority men
and women receive lower returns on their human capital investments in ethnically segregated
areas.
Discussion
In this study we used raw data as well as multi-level analysis in order to study the influence of
segregation and ethno-religious background on the transition from school to work among in
England and Wales. The analysis has revealed significant differences between the picture
obtained from the raw data and the multilevel models. For example, while on the one hand
Figure 1 suggested that Chinese people are more likely than any other group to encounter the
lowest level of returns, the multi-level analysis on the other hand has revealed that Chinese are
in fact not different from CWB and their relative position is better than all the other non-white
groups. Similarly, from the raw data in Figure 3 MB women seem to be under-represented
within the highest level of transition; however Table 1 suggested a completely different picture
with MB women being significantly more likely than CWB women to be represented within
the highest level of returns. This suggests that the multi-level analysis is more accurate as it
takes into account other factors at both levels, individual and contextual. Therefore in this
section, we will base our discussion upon the multi-level analysis.
The two main predictors in this study were ethno-religious background and residential
segregation. We will discuss first the impact of the ethno-religious background and then move
on to discuss the impact of segregation. In relation to the ethno-religious differences, the
findings of this study confirm that these differences are indeed very complex and perhaps more
complex than has been revealed by previous studies such as the 4th National Survey of Ethnic
Minorities (Modood 2005; Modood, et al. 1997) or the studies carried out by Heath and
McMahon (1997; 1999). For example, we have seen that JWB and CWI men and women are
as successful as the majority CWB in converting their qualification into the highest level of
returns and avoiding the lowest level of transition score. At the other end of the scale, we found
CBA to be the most disadvantaged group (for both men and women). This group is the only
minority to be significantly less advantaged than the majority CWB in all models placing them
at the bottom of the scale. It seems that CBA encounter severe difficulties in converting their
qualification into salaried jobs as pointed out by Heath and McMahon (1997). It is possible that
the language skills and length of stay may explain some of these disadvantageous, nevertheless
19
other groups who may encounter similar language barriers are less disadvantaged such as MB
for example, suggesting that the explanation might lie elsewhere. In explaining the
disadvantaged position of the CBA one may include social networks and job seeking patterns
as well as type of qualifications and not only level of qualifications. Recent studies have
pointed out that the educational profile of this group is very high with a large proportion
having higher qualifications (Khattab 2005). Yet, many of these qualifications have been
obtained overseas (in the countries of origin) and because many employers in the UK do not
recognise these qualifications as equal to UK qualifications, CBA fail to convert them into
prestigious jobs in the labour market.
CBA men and women are not the only group to be disadvantaged in the transition from school
to work. All non-white ethno-religious groups are underprivileged in terms of their transition
outcome including all four Muslim groups, HI, SI and CBC. The pattern that emerges for the
models of men and for women here is that non-white ethno-religious groups encounter more
penalties than the other white groups, with Chinese women and MB women as the only
exceptions. This general pattern might lend support to the colour and culture racism theory
suggested by Modood (2005). While the fit with a hypothesis of colour-discrimination is
evident, it is less so in relation to the cultural aspect. [But the real test would be of Black
Caribbean’s and Black British (colour-racism) with the others (colour-racism and cultural
racism)]. It is true that the coefficients for the Muslim minorities (Table 1) tend to be slightly
higher than the coefficients for HI and SI, but the question this raises is whether the differences
in the size of coefficients reflect more substantial differences between these groups? In order to
examine this latter point we ran two extra models, one for Muslim men and one for Indian men
(Tables 2 and 3). The results of these two models confirm that significant differences do exist
between non-white groups. For example, in terms of religious differences within the same
ethnic group, SI are significantly different from HI. They are more likely to receive the lowest
return level than HI placing them even below MI who are not different from HI. This finding
about the disadvantaged position of SI (relative to HI and MI) lends support to Khattab's
conclusion that SI were more disadvantaged than HI and MI in converting their academic
education into non-manual jobs (Khattab 2005). It also supports Platt’s findings in relation to
the differences between HI on the one hand and MI and SI on the other hand in accessing
professional and managerial classes (Platt 2005b). She found that both MI, and to a lesser
extent SI, were less advantaged in accessing professional and managerial classes than HI.
Table 2 and 3 about here
20
In terms of ethnic differences within the same religion, Table 3 shows that MI men are
significantly less likely than MP men to encounter the lowest level of returns to education.
Being MA or MO rather than MP significantly increases the log-odds of the lowest transition
score. Unlike the former groups, MB and MWB men are as likely as MP to experience the
lowest transition score. Thus, within the Muslim minorities there are a clear hierarchy in terms
of oppression or disadvantage, MI clearly are the most advantaged Muslim group, whereas MA
and MO are the most underprivileged groups with MP, MB and MWB men being somewhere
in the middle between the other two groups. The fact that MA seem to be the most
disadvantaged group within the Muslim minorities may lend support to the supposition of a
cultural-racism on top of a colour-racism. This theory may also gain support from the main
finding in relation to the relative disadvantageous position of SI men. All the three main Indian
groups (MI, HI and SI) are visible minorities in terms of their skin colour, yet, SI are probably
even more so (from the cultural point of view) as many Sikh men wear turbans (as a religious
symbol), whereas HI and MI do not wear them, which would make them less visible. One
might argue then that SI could (theoretically) face more hostility than the other two Indian
groups. The empirical evidence in this study can be interpreted in this way.
The Chinese, on the other hand, are an Asian minority with a different physical appearance
(compared to the majority CWB), yet in terms of skin colour and cultural distinctiveness, they
might be less visible than the other black and Asian groups, and therefore they are less likely to
encounter the same level of discrimination and hostility. This of course can only be part of the
explanation why are they so successful, and no doubt that their educational profile and the kind
of qualifications they obtain (in terms of areas and subject matter) should also be taken into
account in explaining their successful. Notwithstanding, Chinese women were one of the most
polarised groups along with BM women. While both groups were more likely than CWB
women to be represented in the lowest level of returns to education, interestingly these two
groups were the only groups to be significantly more likely than CWB women to receive the
highest transition score (3/4). Their similar pattern does not stem from their similar educational
and employment profiles; on the contrary they are very different from each other. For example,
while 69% of all Chinese women aged 20-24 are in full-time education, the comparable
percentage among MB women is 16% only. In terms of higher qualifications, 52% of Chinese
women aged 20-29 hold higher qualifications, whereas 12% only among MB women (Khattab
2005). Thus it is not evident why despite the differences between these two groups, they came
out with similar pattern of transition from school to work. What needs to be explained is why
these two groups were more likely to be represented within the highest transition score than
21
CWB women. The fact that both were also more likely to face the lowest transition score is in
line with the other ethno-religious groups and can be attributed to racial and cultural
discrimination. For Chinese women, the explanation might be found in their employment and
labour market engagement. As was found in the 4th National Survey of Ethnic Minorities,
Chinese women were almost as twice as likely to be in the top professional, managerial and
employers category (compared to British white) (Modood, et al. 1997). Moreover, in a recent
study looking at ethnic minorities in the labour market, it has been pointed out that among
women, only Chinese women stand out with a high proportion of small employers and ownaccount workers, 18 per cent of all Chinese employed people (Simpson, et al. 2006).
With respect to MB women, we suspect that the combination of number of conditions or
circumstances might have influenced their transition into work so that they have become overrepresented within the highest level of transition. Firstly, the high spatial concentration of
Bangladeshis in few areas in the country such as in Tower Hamlets in the East End of London.
Secondly, the initial very low participation level in the labour market and the tendency to work
locally and close to home; and paradoxically, the low level of qualifications, and in particular
high proportion without any qualifications (70% of MB women aged 20.29 have no
qualifications). The high residential segregation of MB and the existence of many
governmental and community projects and services for the Bangladeshi community in Britain
along with a valuing of people with suitable linguistic and cultural backgrounds to deliver
those services, may go some way to explain the anomaly.
As with respect to the influence of the macro level factors, it is worth noting that while the
impact of IMD was inconsistent, the MII had more solid influence indicating a negative
association between levels of ethnic segregation and returns to qualifications. One might argue
that the inconsistency in the IMD might be due to a multicolinearity problem between the two
level-2 factors. We have measured the correlation between both variables and it was not high
(R=0.23), suggesting that these two variables are not the same and measure two different
things. While we do rule out the possibility that this result is simply an error, it is also possible
that this result stems from a gap between the demand side and supply side of labour in deprived
areas. If we assume that the more educated and affluent people would be less attracted to live
in deprived areas, for any job (especially jobs requiring high qualification) there would be
fewer candidates with appropriate qualifications competing for these jobs than in affluent and
less deprived areas. Employers may be willing to offer attractive contracts in order to attract
‘good’ candidates, but it is also possible that these employers would offer the jobs to local
22
people whose qualifications do not necessarily match the skill level required by the jobs. For
example, in many deprived areas, for this matter in ethnically segregated areas as well, there
are many projects and services that has been launched to cater for and help local communities
such as “Bridging the Gap”, “Connexions”, local youth clubs, community centres and so on.
Many of these projects and community centres focus on creating new employment
opportunities for local members of the communities. If we assume that in these local
communities (themselves deprived), higher qualifications are rare, then it is possible that some
people, especially women might benefit from these opportunities by receiving relatively high
returns to their qualifications. MB women may very well fit into this framework and that their
high returns may be well explained using this kind of argument.
As we mentioned above, the evidence presented earlier in relation to the impact of segregation
has suggested that higher levels of minority segregation are associated with lower levels of
returns to qualifications, or lower transition scores. This finding is in line with previous
research about the impact of segregation on labour market outcomes and particularly on
unemployment (Fieldhouse and Gould 1998; Fieldhouse and Tranmer 2001; Khattab 2006). As
previous studies on segregation and housing patterns of ethnic minorities in Britain has pointed
out that to some extent the segregation of minorities was a result of socio-economic out of
constraints (Modood et al, 1997), the lower returns to education resulted from segregation
might lend support to Massey’s argument that high levels of forced segregation restricts the
socio-economic opportunities of minority groups through channelling them into deprived
neighbourhoods (Massey, 2002: 351). Although forced segregation is an important factor in the
economic undermining of minorities, yet whether their segregation developed in the form of
ghetto or enclave is an even more important factor that can cancel out the effect of the
involuntary segregation as in the case of Venetian Jewish ghetto ((Bernasconi 2002).
However, the low levels of returns to education associated with segregation do not tell us how
developed or poor the social capital, economic activities and resources are within these
communities. Nor do they tell us whether the communities are developing as economic
enclaves as described by Portes and Manning (2001) or ghettos as discussed by Zhou (2005).
Because there are substantial differences between the ethno-religious groups in their economic
and social circumstances as well as in their levels of segregation and housing patterns
(Johnston, et al. 2006; Johston, et al. 2004; Owen 2003; Peach 2005; Peach 2006; Smith, et al.
forthcoming), it is possible that some minorities are closer to the model of economic enclave,
whereas others becoming more ghetto-like. To examine this latter proposition, we need a more
23
qualitative study that looks at these segregated areas through case studies mapping the social
capital resources and identifying the economic activities exist in these communities. Such a
study would be very useful in explaining some of the findings of this study.
Conclusion
In this study we were concerned with the question of how ethno-religious background and
residential segregation affect the transition from school to work measured by the distance
between someone’s formal qualification and the actual skill level required for his or her
occupational class. Due to the differences between men and women, we have fitted different
multi-level models for both genders. The models fitted for men and women have revealed that
combining both ethnicity and religion was useful in exploring the complex nature of the
differences between the groups. These models have provided an empirical evidence for the
existence of multi-dimensional penalties or discrimination, one associated with skin colour and
one associated with cultural difference.
Residential segregation was found to be associated with the level of returns to qualification
supporting the argument that high levels of segregation restrict economic success and
significantly lowering the returns for qualifications. From these findings we only can conclude
about the negative influence on returns to qualifications, but we should draw no conclusions
about employment status, the scale of economic activity taking place nor levels of social
capital. It is possible that within segregated areas, members of the minority groups may be able
to find local jobs either as employees or as self-employed and to avoid unemployment; yet
whether then they can also secure high returns on their qualification is a different question, and
as the findings of this study suggest, the answer to that is no.
24
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26
Figure 1: ISCO by ethno-religious groups, men and women
100
90
80
70
60
50
40
30
20
NRWB
Chinese
CBA
CBC
JWB
SI
HI
MB
MP
MI
CI
0
CWB
10
Legislator, Senior Officials and Managers
Professionals
Technicians and Associate Professionals
Clerks
Service Works and Shop and Market Sales Workers
Skilled Agricultural and Fishery Workers
Craft and Related Trades Workers
Plant and Machine Operators and Assembellers
Elementary Occupations
27
28
Figure 2: Lowest, match and highest returns (transition score) by ethno-religious groups - Men
40
35
30
25
20
15
10
5
0
MI
MP
MB
MO
JWB
HI
Lowest return - 3&4
SI
Chinese
Match position
NR
CWI
CBC
CBA
Others
Other
White
Christian
White
British
Highest return 3&4
29
Figure 3: Lowest, match and highest returns (transition score) by ethno-religious groups - Women
50
45
40
35
30
25
20
15
10
5
0
MI
MP
MB
MO
JWB
HI
SI
Lowest return - 3&4
Chinese
Match position
NR
CWI
CBC
CBA
O
OWB
CWB
Highest return 3&4
30
31
Table 1: Multilevel logistic regression (log-odds) contrasting the lowest and highest transition
scores (-3/-4) against the match score (men and women)
Men
Independent variables
Lowest vs.
match
Model I
Highest vs.
match
Model II
Women
Lowest vs.
Highest vs.
match
match
Model III
Model IV
Level 1
0.054
0.78*
0.013*
-0.38*
-0.25*
0.05*
0.40*
0.50*
-0.01*
-0.33*
-0.00
0.06*
1.12*
1.27*
1.17*
1.52*
-0.08
0.91*
1.10*
0.45
0.12
0.37
0.76*
2.01*
0.50*
0.69*
-0.47
-0.07
0.32
-0.17
0.10
-0.08
0.18
0.60*
-0.40*
-0.11
0.11
-1.40*
-0.16*
-0.44*
-0.51
1.24*
1.51*
1.27*
0.59
0.96*
1.01*
1.46*
0.21*
-0.27
0.94*
1.66*
0.65*
0.82*
0.36
-0.15
0.88*
0.08
-0.15
0.17
0.29
0.77*
-0.28*
-0.21
0.08
-0.91*
-0.09
-0.38
0.013*
0.18
0.006*
-0.62*
0.003
0.81*
0.02*
-0.59*
0.07
0.08
0.11
(0.03)
(33.64)
(0.04)
* p<0.05, Source: CAMS Individual data drawn from the 2001 UK Census.
0.05
(0.03)
Overseas born
Unmarried
Age
Ethno-religious
background (base: CWB)
MI
MP
MB
MO
JWB
HI
SI
Chinese
NR
CWI
CBC
CBA
OW
O
Level 2
Index of deprivation (ID)
Modified index of
isolation MII
Level 2 variance
32
Table 2: Multilevel multinomial regression (log-odds) contrasting the lowest and highest
transition scores (-3/-4) against the match level amongst Indians** (Men)
Independent variables
Lowest vs.
match
Level 1
Ethno-religious
background (base: HI)
BI
MI
SI
OI
NRI
RNSI
CIN
Overseas born
Unmarried
Age
1.37
0.16
0.38*
-0.61
0.29
0.39
0.04
-0.24
0.56
0.02*
Level 2
Index of deprivation (ID)
Modified index of
isolation MII
0.011*
1.21*
Level 2 variance
0.14
* p < 0.05 , Source: CAMS Individual data drawn from the 2001 UK Census.
** BI, Buddhist Indians; OI, other Indians; NRI, no religion Indians; RNSI, religion not stated; CIN, Christian
Indians
33
Table 3: Multilevel multinomial regression (log-odds) contrasting the lowest and highest
transition scores (-3/-4) against the match level amongst Muslims** (Men)
Independent variables
Lowest vs.
match
Level 1
Ethno-religious
background (base: MP)
MWM
MI
MB
MA
MO
MWB
0.178
-0.43*
-0.20
0.46*
0.39*
-0.08
Overseas born
Unmarried
Age
0.13
0.56*
-0.001
Level 2
Index of deprivation (ID)
Modified index of
isolation MII
0.009*
0.60*
Level 2 variance
* p < 0.05, Source: CAMS Individual data drawn from the 2001 UK Census.
** MWM, Mixed white Muslims; MA, Muslim Africans; MWB, Muslim white British
34
Appendix I
ISCO-88 major groups and skill level
Major occupational group
Legislator, Senior Officials and
Managers
Professionals
Technicians and Associate Professionals
Clerks
Service Works and Shop and Market
Sales Workers
Skilled Agricultural and Fishery Workers
Craft and Related Trades Workers
Plant and Machine Operators and
Assemblers
Elementary Occupations
Unemployed/inactive
Skill level
Qualification level
level 4
Level 4/5 (Degree+)
level 3
Level 3 (A/AS level)
level 2
Level 2 (O level,
GCSE grade A-C)
Elementary (1)
Level 0
Level 1 (GCSE grade
D-G)
No qualification
Skill level – qualification level = transition score (return to education)
Range: -4 to +4 with 0 indicating complete match between skill level and
qualification
35
Appendix II
The equation used for the analysis
36
Appendix III
Returns to education, 16-64 by sex, England and Wales, 2001
Returns to education
Ethno-religious
identity
Muslim Indian
Sex
-4
-3
-2
-1
0
1
2
3
Total
4
Male
3.9
5.6
10.4
10.9
36.0
12.2
15.9
1.5
3.5
1258
Female
6.0
4.1
14.1
13.7
44.0
8.8
6.7
1.1
1.7
1124
Male
5.1
6.0
13.2
11.8
30.4
12.4
17.4
1.6
2.2
5933
Female
7.5
6.3
14.4
14.6
44.9
6.1
4.9
.7
.7
5360
Muslim
Male
4.0
5.1
12.4
11.5
29.6
11.3
21.7
1.9
2.5
2212
Bangladeshi
Female
4.4
5.8
14.0
14.6
49.8
6.0
4.3
.5
.6
1992
Muslim others
Male
7.7
8.9
19.0
17.3
32.7
8.1
5.1
.6
.6
943
Female
8.5
7.3
15.8
18.3
39.7
5.5
3.3
1.0
.5
796
Male
3.5
4.7
8.4
10.2
37.1
12.7
14.4
5.5
3.5
1929
Female
6.4
6.2
13.6
15.5
33.0
12.0
9.6
2.3
1.4
1825
Male
4.5
5.8
11.8
10.9
39.5
10.7
11.5
2.5
2.8
4842
Female
8.3
5.7
15.4
13.7
33.4
10.8
9.8
1.2
1.6
4775
Male
3.5
5.6
11.4
12.3
30.4
15.1
15.6
2.5
3.5
2986
Female
4.7
5.7
13.8
14.6
32.5
13.9
11.4
1.2
2.3
2973
Male
11.4
11.0
12.0
10.9
30.4
6.4
13.2
1.1
3.7
2438
Female
13.7
9.1
15.2
14.9
27.5
6.8
9.0
1.2
2.6
2633
No-religion White Male
2.7
3.3
8.2
12.9
36.2
18.2
13.7
3.5
1.3
76594
British
Female
3.5
3.9
11.4
18.2
36.2
14.9
9.2
1.9
.7
58968
Christian Irish
Male
2.9
2.8
6.1
10.6
39.6
16.4
15.8
3.0
2.8
4835
Female
5.3
3.5
9.2
18.9
35.3
13.2
11.8
1.3
1.3
4925
Christian
Male
2.7
2.9
9.6
13.6
32.2
19.8
14.6
3.5
1.1
3231
Caribbean
Female
4.8
3.4
13.0
21.4
29.0
16.9
8.7
2.3
.4
4562
Christian African
Male
10.2
10.5
21.3
18.4
30.8
4.5
3.5
.6
.2
3025
Female
10.9
7.1
22.3
22.2
25.9
7.1
3.5
.8
.2
3568
7.3
5.9
11.3
13.8
37.3
11.0
9.7
2.0
1.7
27915
11.4
7.2
14.8
18.0
32.1
8.6
6.2
1.0
.7
29434
Muslim Pakistani
Jewish
Hindu Indian
Sikh Indian
Chinese
Others
Male
Female
Other White
Male
3.3
4.7
8.7
12.9
34.4
17.0
14.0
3.6
1.5
39750
British
Female
3.8
4.3
11.3
16.4
34.4
15.3
11.4
2.1
1.0
29855
Christian White
Male
2.0
2.0
6.8
10.4
34.1
20.4
17.7
4.6
2.1 282221
British
Female
2.9
2.5
9.4
15.9
34.5
18.2
13.0
2.4
1.1 301057
TOTAL
Source: CAMS Individual data drawn from the 2001 UK Census.
37