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Cite as: Kislev, E. (2016). The Effect of Education Policies on Higher-Education
Attainment of Immigrants in Western Europe: A Cross-Classified Multilevel Analysis.
Journal of European Social Policy. 26(2):183-199.
Source: Journal of European Social Policy, 26(2):183-199
Find on: ResearchGate.com, Personal Website
The Effect of Education Policies on Higher-Education Attainment of Immigrants in
Western Europe: A Cross-Classified Multilevel Analysis
Elyakim Kislev, The Federmann School of Public Policy and Government, The Hebrew
University
Abstract
The number of immigrant students in Western Europe is growing steadily, but their social
integration and educational achievements are still lagging behind. Nevertheless, there is
still very little empirical evidence on which policies can effectively promote them. Thus,
this paper tests two main types of policies: targeted support and intercultural policies, and
compares their effect on university graduation of six immigrant groups in thirteen
Western European countries. This research incorporates country- and origin-based
variables as well as social and individual characteristics in cross-classified multilevel
analyses. Data from the European Social Survey, the Migrant Integration Policy Index,
the UN database, and the World Bank database are integrated here. Findings show that
intercultural policies have more positive effect on immigrant students than targeted
policies. Furthermore, there is division between these six groups not only in their actual
educational achievements, but also in the extent at which they are helped by education
policies.
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Keywords: education; discrimination; immigration; multilevel analysis; Western Europe.
Introduction
The recent flows of migration to Western Europe make political and social integration of
immigrants crucial in order to avoid the creation of an underclass. Studies in the US (see:
Portes and Zhou, 1993; Zhou, 1997) show that a downward mobility among children of
immigrants is quite prevalent in later waves, and this might be the case for Western
European immigrants as well. However, these studies also argue that education is one of
the main keys for economic and social integration of second- and third-generation
immigrants. Besides the benefit of professional preparation for the labor market,
education provides immigrants with the possibility of language acquisition, social
networking, and familiarity with the host country’s culture (Crul et al., 2013; Fossati,
2011). Moreover, the education system is a main venue for the formation of identity and
sense of belonging among immigrants (Verkuyten and Thijs, 2002; Verkuyten and Yildiz,
2007).
Nevertheless, most immigrant students in Western Europe do not perform as well
as the native population. For example, results from PISA (Program for International
Student Assessment) test show low scores in reading and mathematics among first- and
second-generation immigrants. Marks (2005) measures results in 20 countries, of which
11 are part of Western Europe and included in this current paper. He finds that the
average gap among first-generation immigrants of those 11 countries in reading is 63
points lower than the respective majority group (the OECD average in each domain is 500
and the standard deviation is 100). In mathematics, it is 59 points lower. Among second-
generation immigrants who speak the national language, scores in reading were 53 points
lower and in mathematics scores were 57 points lower. These education gaps continue to
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affect immigrants in the labor market (Heath et al., 2008). Studies show how being an
ethnic minority is coupled with high rates of unemployment, lower salaries, and worse
opportunities for promotion in the workplace (Heath, 2007; Heath and Cheung, 2006;
Kogan, 2006).
Due to these difficulties and poor results different Western European countries
have started to enact various types of policies in order to better integrate immigrant
students into the education system. Yet studies in this field of immigration policies are
still scarce and cross-national comparative data is limited (see: Collett and Petrovic, 2014;
Fleischmann and Dronkers, 2010). Moreover, policies have many direct and indirect ways
of influencing immigrants’ integration and it is very hard to gauge their efficiency. Even
more problematic is the fact that policies are enacted on the country level, whereas
researchers and policymakers are interested in their effect on both the country and
individual levels. In other words, it is not enough to solely measure the effect of migration
policies on countries’ demography and economy, it is also vital to investigate how
immigrants themselves assimilate and which policies support their integration.
Hence, this current paper contributes to this very recent literature and examines
the effect of education policies towards immigrants on their higher-education attainment.
Since many resources are invested in promoting these policies, it is critical to ask which
policies work and to identify the populations that were most influenced by them. There
are two types of policies in the center of the debate on how to help immigrant students:
policies advancing a targeted support towards immigrants and policies advancing an
intercultural environment. In this paper, I compare the effect of these two types of policies
on six immigrant groups in thirteen Western European countries.
Education Policies
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Dupriez and Dumay (2006) divide education policies towards immigrants between an
ethos of differentiation and an ethos of integration. The ethos of differentiation allocates
special curricula to immigrant children, while the ethos of integration tries to incorporate
immigrant children into the regular program and keep them with native students. Side by
side, the integration policy provides additional hours (beyond the regular ones) and/or
complementary schools targeting the special needs of immigrant children. Dupriez and
Dumay (2006) argue that the integration ethos is dominant in the Scandinavian countries.
In contrast, the ethos of differentiation is prevalent in Germany, Austria, Switzerland,
Luxemburg, and the Netherlands.
Indeed, it is quite expected to see that the integration approach yields better
outcomes. An analysis of PISA results (Dronkers et al., 2012) shows that the integration
approach produces higher achievements for immigrant children. It was also found to
reduce the effect of parents’ cultural capital and thus provide a higher equality of
opportunities to immigrant children, which does not depend on their origins (Dupriez and
Dumay, 2006). Therefore, the EU adopted this approach to its agenda and the current
suggestion is ‘mainstreaming integration’. This approach means making integration of
immigrants a part of the various public services’ programs and not part of special
programs for immigrants (Collett and Petrovic, 2014; European-Commission, 2014).
However, favoring the integration approach is only the first step in a long
investigation. It is still unclear what integration means exactly in terms of policymaking
(see previous attempts: Niessen and Schibel, 2007). In other words, the more relevant and
a harder to address question is which component of the integration approach is more
helpful. Integration of immigrants is composed of two main components that mix with
each other in reality: the intercultural component and the targeted support component.
Hence, it is important to test the effect of each on its own.
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Naturally, both policies are needed and a combination of policies is required to
some degree. However, a more modest question is being asked here as to the ‘marginal
profit’ of these two types. Due to the rising frustration of the European public in regards
to immigrants, on one hand, and budget constraints in times of financial recovery, on the
other hand, it is crucial for policymakers to know the most effective ways in advancing
immigrants. If research succeeds in distinguishing the two and test their efficiency
separately, they will have more evidence upon which to base further measures and
policies.
Targeted Support Policies
Targeted support policies focus on the special needs of immigrants and directly help them
with the difficulties they face. Targeted support includes measures such as special classes
for learning the local language and classes to catch up with the local standards in various
fields (e.g. mathematics). For children of immigrants, who were born in a Western
European country, such classes help to compensate for the lack of education resources
within the family (Crul, Schneider and Lelie, 2013; Gofen, 2009; Gofen and Blomqvist,
2013).
Targeted support policies, however, have proven to bear some disadvantages.
Some studies (see: Fossati, 2011) argue that targeted support has a counter effect that
actually hurts immigrants. The suggested explanation for this is that targeted policies
single out immigrants and segregate them as well as produce inter-communal tensions due
to the ‘special benefits’ immigrants receive. The consequences are that these immigrants
feel more excluded and have a higher sense of discrimination, which, in turn, lowers their
achievements. Furthermore, other studies (see: Roessingh, 2004) argue that language
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programs directed to immigrants are effective but often times suffer from poor
implementation as extra-curricular programs.
Due to these problems, another suggestion in the policy discourse is to implement
an indirect targeted support to immigrants and non-immigrants alike (Collett and Petrovic,
2014). Such support provides special help to all underprivileged communities regardless
of their ethnic and place of birth background. However, the difficulties in this kind of
policies are lack of focused and tailored programs and costly implementation due to the
larger audience they target.
Intercultural Policies
Thus, the other suggested approach in advancing immigrants is intercultural policies that
promote integration of immigrants into mainstream society while both natives and
minorities are encouraged to know and accept each other's culture. Essentially, this
competing approach suggests that the social position of the ethnic group in the host
society should be the focus of integration processes.
Indeed, many studies show that discrimination in the education system in Western
Europe is prevalent. In the UK, Gillborn (1997) shows that black and Asian students have
much higher rates of school exclusion due to teacher stereotypes and racial harassment
from other students. This qualitative review shows the nuances in this social exclusion.
While blacks are perceived as loud, aggressive, and academically weak; Asians,
especially girls, are perceived as passive and 'easy to be ignored'. A study on immigrants
in the EU (Kjerum, 2009) shows that 11% of German Turks felt discriminated against by
school personnel at least once in the 12 months preceding the survey. Another study
(Hermans, 2004) on Moroccans in the Netherlands and Belgium, shows that the notion of
temporality of Moroccan students causes teachers to see them as a burden, since they are
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perceived as children of guest workers. Teachers also tend to ignore these students and
show less interest in them (Ceobanu and Escandell, 2010). Furthermore, Moroccan
parents see teachers as racists, full of distrust, and within an enduring conflict against
Islam (Hermans, 2004).In the Netherlands, a study (Verkuyten and Thijs, 2002) on
Turkish, Moroccan and Surinamese children, shows that these perceptions are true also
when it comes to relations between students and their peers.
These phenomena of discrimination in the educational system prevent minorities
from achieving higher in direct and indirect ways even after accounting for
socioeconomic background (Rothon, 2007). Studies show that social distance and
negative stereotypes about minority groups undermine their performance (Portes and
Rumbaut, 2006; Shih et al., 1999; Steele, 1997). Other studies by Berry and Kim (1988)
and Berry and Sam (1997) show that a stance of integration is correlated with high self-
esteem and high educational achievement. In indirect ways, studies show that
discrimination and prejudice lead to a defensive detachment of the self from the
educational system and the school and, therefore, lower higher education attainment
levels (Verkuyten and Thijs, 2004; Verkuyten and Yildiz, 2007). Kristen (2005) shows
that immigrants’ parents lack the information resources about the educational system and
this prevents them from making the optimal decision for their children.
Moreover, even when minorities ostensibly have the same level of educational
capital (e.g. BA degree), the discrimination factor determines the quality of this capital.
To wit, the institutions in which minorities acquired their education are usually less
respected and their studies are less applied and valued in the labor market. The literature
on the quality of scholarly degrees among various groups (see: Gesemann, 2007; Heath,
Rothon and Kilpi, 2008; Leslie, 2003) clearly shows how majority groups study in better
schools, which is determined, many times, by place of residency. This leads them to be
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accepted to better universities and earn more desirable professional degrees (e.g. applied
degrees such as engineering and computer sciences).
Therefore, to deal with discrimination and its consequences, many researchers
recommend the intercultural policy, which is based on seeing the relations between
minorities and the majority group as the main obstacle. For example, Sternberg (2004)
advocates the development and use of intercultural policies that create a welcoming
environment towards groups from other cultures, such as using culturally sensitive study
materials. Banks (2000) recommends in his writings that nation-states should engage in
citizenship education that contains a diversity component. He also criticizes the lack of
representation of minority groups in many fields, such as history and literature. However,
this approach is mostly US-oriented and not yet widely tested in Western Europe. One
obstacle is that most Western European states are not immigration countries, but rather
nation-based and thus more reluctant to adopt the intercultural approach (Castles and
Miller, 2009). This led to only a recent implementation and thus data is still limited.
Another obstacle is that it is difficult to compare the two tactics of interculturalism and
targeted support since most of the time they are mixed. Finally, although Western
European countries share some similar patterns and circumstances in absorbing
immigrants, they also have some differences affecting immigrant students that should be
accounted for and thus require complex models. For example, in France, children enter
nursery school at the age of two or three and spend a full day in primary school, while in
Germany and Austria they enter at age six and spend half a day in primary school (though
this situation has started to change recently). These differences might result in educational
gaps and language acquisition variations (Crul, Schneider and Lelie, 2013). Differences
also exist in ‘streaming’ students to higher or lower education tracks due to ways in which
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pre-university tests are conducted. This has proven to have a significant effect on
immigrants (Crul et al., 2008).
Therefore, given this lack of empirical testing and its structural difficulties, the
current paper integrates several databases and investigates the effect of multiculturalism
versus targeted support by using a cross-classified multilevel analysis of Western
European education policies. Such an analysis allows for an incorporation of different
countries that use different degrees of each component in their education system. In this
way, one can know whether high levels of one component lead to higher educational
achievements of immigrants than the other component. Moreover, this paper also tests
whether the two components affect different immigrant groups in a different way. For
example, Asians and Sub-Saharan Africans, who suffer also from the visibility factor
(Bonilla-Silva, 2006; Kirschenman and Neckerman, 1991), might find the intercultural
policy more effective than other minority groups find it. This hypothesis will be tested
below as well.
Data and Variables
This research uses the European Social Survey (ESS) together with the Migrant
Integration Policy Index (MIPEX). The appendix of this paper also includes the Gini
index and the Human Development Index (HDI). The ESS has data from 2002 to 2012 in
intervals of two years, though this survey provides specific parents’ country of birth only
from 2004 onwards. I examine six groups of first- and second-generation immigrants to
Western Europe: Sub-Saharan Africans, North Africans and Middle Easterners (MENA),
South Asians, Southern Europeans, Eastern Europeans, and internal Western European
immigrants (individuals who arrived from another Western European country than the one
where the ESS was conducted). First-generation immigrants are only those who arrived at
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age eighteen and below. These six groups comprise the main immigrant populations when
taking into account the relative homogeneity of countries within these regions according
to significant variables such as the Gini index and GDP per capita. Nevertheless, I still
control for the specific country of origin as another level in the cross-classified analyses.
In identifying the populations under discussion, this study uses variables where
respondents mention their parents’ birthplace as well as their own birthplace. For
example, respondents who were born in the country were the survey was conducted, and
for whom both parents were born in a South Asian country, are identified as a second
generation of South Asian immigrants. The majority group of Western Europeans (aka the
native population) is identified as those individuals for whom both parents were born in
the same country where the survey was conducted. Though there are many divisions and
sub-divisions in defining the borders of Western Europe, here I define 13 Western
European countries as participating in this research: Austria, Belgium, Denmark, Finland,
France, Germany, Iceland, Ireland, Luxemburg, Netherlands, Norway, Sweden,
Switzerland, and the United Kingdom.
To account for both the origin and destination countries, I incorporate these
countries as levels 2 and 3 in cross-classified multilevel analyses. In cross-classified data,
lower level units do not belong to one and only one higher-level unit. Rather, lower level
units belong to pairs or combinations of higher-level units formed by crossing two or
more higher-level classifications with one another. Thus, this research analyzes students
as nested within origin countries and separately nested within host countries (see: Van
Tubergen et al., 2004). Naturally, there is no much room to increase the number of host
countries in order to strengthen level 3 in the structure of the hierarchal models without
incorporating countries, in which circumstances of immigrants’ assimilation are
fundamentally different from those in Western Europe. Nevertheless, research shows that
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there should not be significant divergence in the results where there are more than 10
nests in the higher levels (the ML estimates are slightly downwardly biased), especially
since the presented models use robust standard errors (see more: Kline, 2011; Snijders,
2011). Moreover, the results presented here closely resemble those yielded from two-level
models (with only origin countries) as well as from fixed-effect-only models (see
Appendix). The cross-classified multilevel analysis method was chosen, however, in order
to better reflect the multiple influences on these immigrants, and, indeed, this method was
found more appropriate in likelihood-ratio tests.
In comparing the six populations, I use both demographic and socioeconomic
variables in order to understand the characteristics of these groups in the descriptive
section. I use the gender, age, marital status, and citizenship status variables. Economic
measures that are included here are labor-force participation and unemployment. As
education variables, I use ‘having a BA’ and average years of education. To measure
parents’ education, this paper includes measures of high-school graduation for both the
father and the mother. For language, I use a dummy variable, whether respondents speak
the local language/s of the specific country in which they reside as their first spoken
language at home. Note that there is no variable of the level of speaking the local
language in the dataset. In addition, there is no reliable variable for the exact parent’s
socioeconomic status. The respondents’ household income variable, although positively
correlated with higher achievements, does not change the results investigated here and
was excluded due to high number of missing data.
To account for the social environment, two yes/no questions are included: ‘being
part of a discriminated group’ and ‘being part of a minority group’. Feelings of being a
minority do not always overlap with feelings of discrimination, as will be shown below,
due to identification reasons that affect the reports, such as the visibility factor. Moreover,
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these two questions are still not enough because self-reports on social exclusion and their
relation to educational achievements is potentially endogenous. For example, respondents
may feel as a minority because they are highly educated and exposed to racial and ethnic
studies that develop their identification as a minority (Nagel, 1994). Therefore, another
measure of social acceptance towards immigrants is incorporated here from the native
population’s perspective. This is through the question of whether immigrants make the
respondents’ country a worse or better place to live. Answers to this question are specific
to each host country in each ‘year of survey’ and were estimated only from the native
population (see Appendix A2 for detailed figures by country).
In the multilevel analysis, I control for the relevant independent variables together
with the survey year. Note that the socioeconomic background of the respondents might
also have an effect and I have checked all models with the most relevant variable
indicating the income level of the respondents – the household income. Unfortunately,
there is no other variable for income in the dataset, nor variables for parents’ income or
socioeconomic status. The only two variables on the socioeconomic status of the parents
in the European Social Survey are the employment status of the parents when the
respondent was age 14, and the type of occupation they hold. Naturally, this does not help
much and, thus, one can only use the level of the household income of the respondent.
Indeed, adding this variable to the equations does not change the results. The main
difference is that this addition reduces the number of cases significantly and hence
reduces the statistical significance of the results. In addition, the income variable is
positively correlated with educational attainment as known in the literature, but is not
enough to explain immigrants’ disadvantage as also known in previous studies (see
Rothon, 2007 for example).
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The reception environment in the destination country is also included by the
question of whether immigrants make a country a worse or better place to live. Answers
to this question are used here only from the native population, excluding first- and
second-generation immigrants, and a different result is calculated for each country in each
survey year separately. In Appendix A1, further indicators of the host countries are
included, which are the HDI and the Gini indexes (see Appendix A2 for detailed figures
by country). All calculations herein were made with the population and design weights
provided.
The main variables that are put to a test here are the degrees of two sets of
policies: intercultural policies and targeted support policies. These measures come from
the Migrant Integration Policy Index (2013), which collects rankings measured by
independent scholars, practitioners in migration law, education, and anti-discrimination in
every country (see Appendix A2 for detailed figures by country). Criteria for intercultural
policies include state support for public information initiatives to promote the
appreciation of cultural diversity throughout society and within schools; modification of
the school curricula and teaching materials to reflect changes in the diversity of the school
population; adaption of daily life at school based on cultural or religious needs in order to
avoid exclusion of pupils; a special effort to diversify the teacher workforce; and teacher
training that includes the appreciation of cultural diversity. Criteria for targeted support
policies include continuous and ongoing education support in language(s) of instruction
for migrant pupils; systematic teaching assistance and homework support for immigrants;
and addressing migrant pupils' learning needs, teachers' expectations of migrant pupils,
and specific teaching strategies to address this in teacher training.
These relevant education policies were ranked in 2010 and reflect different
degrees at which policies towards immigrants were implemented in different Western
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European countries. Therefore, all calculations herein were made on the 25-34 age groups
of second-generation immigrants because they graduated in the period for which ranking
of policies refers. Note that policies of 2010 still do not refer exactly to this age group.
This concern definitely necessitates future investigations in order to verify the results
presented hereby. Yet when looking at other rankings of policies where there is also data
from 2007 (e.g. anti-discrimination and labor market policies), one can see only slight
changes if any between the years, and this might hint that the results would not have been
much different if the policies were ranked earlier. In addition, a sensitive analysis was
made for different age cohorts (e.g. 24-33 or 26-35), and produced similar results.
The policy variables are included as further steps in five models of cross-classified
multilevel analyses. I measure each policy effect separately and then both of them
together in the same model. I also incorporate an interaction between the policy ranking
and each ethnic group in order to test the effect of a given policy on each group of
immigrants separately. Finally, note that I have also tested for a possible interaction
between the two different types of policies since it might be the combination of policies
that leads to more or less effective results. However, such interaction does not show a
significant effect and therefore is not included here.
Findings
Demographic, Social, and Economic Characteristics of Young Immigrants in Western
Europe
Before examining the various education indicators of these young immigrant groups,
Table 1 presents their main demographic, social, and economic characteristics. It can be
seen that marriage rates are significantly higher among immigrants compared to natives,
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especially among non-European immigrants. This cultural characteristic might have an
influence on immigrants’ higher-education attainment. Marriage entails more housing
expenses on average due to the tendency to live outside of parents’ homes and thus paying
rent or taking a mortgage. Therefore, immigrants might rush to the labor market in order
to provide sufficient income to their household instead of a long-term investment in
higher education. Hence, the regressions below were estimated also with marital status,
which might influence the difference between the groups. However, this has not proven to
have a significant effect on the outcomes of the policies under examination and thus
excluded from the regressions in Table 3, though the regressions in Appendix A1 fully
include this variable.
Table 1: Demographic, Social, and Economic Characteristics: Immigrant Children and
Second-Generation Immigrants in Western Europe, Age 25-34 [about here]
Table 1 also shows that non-Europeans and Eastern Europeans present a
significantly higher level of citizenship acquisition than Southern and Western Europeans.
Indeed, the proximity, geographically and politically, of Southern and Western Europeans
might explain their tendency not to pursue citizenship. This is because they have the EU
passport, which leads to free movement across borders. Thus, citizenship holding must be
accounted for in predicting higher-education attainment, but it is unknown what the effect
should be. The main question is whether holding a citizenship will ease the entrance to the
higher education system or is it insignificant compared to other, less legal and more
social, variables. Note that there is not exact data on EU passport holding in the ESS and,
therefore, conclusions can only be derived indirectly.
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Table 1 continues to present data on the percentage of second-generation
immigrants within these ethnic groups. South Asians and Eastern Europeans present very
low numbers of second-generation immigrants, while Southern and Western Europeans
present the largest share of second-generation immigrants. Indeed, although Western
Europe has started to experience growing flows of migration immediately after the
Second World War due to the need for reconstruction, Southern European countries were
the main source of these workers at first. Permissions were extended only in the 1960s to
include a 'second tier' of countries including Turkey, India, and North African countries.
Later, the dissolving of the Soviet Union and adding several Eastern European countries
to the EU brought another major wave of immigrants from these countries. These gradual
processes might explain variations in the share of second-generation immigrants.
In addition, Table 1 shows socio-economic stratification of these groups. Similar
to previous research, unemployment rates are very high among Sub-Saharan Africans,
North Africans, and Middle Easterners. South Asians and Eastern Europeans are
thereafter, followed by Southern and Western Europeans. The latter are quite comparable
to the native population. This stratification repeats in findings of labor-force participation.
The reason for low participation rates among immigrants might be both social exclusion
from the majority group and cultural patterns, where women are excluded from the labor
market due to traditional family structure.
Indeed, this economic stratification relates to that of discrimination. Southern and
Western European immigrants show very low levels of feelings of discrimination, levels
that are almost comparable to those shown by the native population, which still suffers
from other types of discrimination such as gender and sexual orientation discrimination.
South Asians and Eastern Europeans report mid-levels of discrimination. Sub-Saharan
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Africans, North Africans, and Middle Easterners experience far higher levels of
discrimination.
This hierarchy of groups according to the discrimination question resembles the
same ranking of immigrant groups by reported feelings of being a minority. Those who
report higher levels of discrimination also report higher levels of feelings of being a
minority; mid-levels of discrimination correspond to mid-levels of feelings of being a
minority, and so on. This holds true with one important exception. South Asians, who
report medium levels of discrimination, present the highest level of feelings of being a
minority. This difference might be explained by the visibility factor that might be more
pronounced for South Asians than North Africans or Middle Easterners. Thus, although
they do not experience such high levels of hostility, they still feel that they are visibly
different from others and therefore identify themselves as a distinct minority group. This
difference between the two measures necessitates taking them both in the analysis below
for better accuracy of immigrants’ social environment.
Education Characteristics of Young Immigrants in Western Europe
Table 2 presents statistics of education levels, parents’ education levels, and language
skills of these groups. In terms of education levels, Southern Europeans show the lowest
rates of BA holding immediately below the MENA group. Sub-Saharan Africans and
Eastern Europeans present slightly higher levels, while South Asians overpass Western
Europeans immigrants as well as the native population. This hierarchy recurs in the
measure of average years of education.
Table 2: Education Characteristics: Immigrant Children and Second-Generation
Immigrants in Western Europe, Age 25-34 [about here]
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Furthermore, Table 2 shows the levels of parents’ education. Unlike measuring
higher-education attainment among the young immigrants, parents are measured here by
high-school graduation since this is more prevalent in their generation and, therefore,
serve as a more reliable measure. Indeed, parents of Sub-Saharan Africans, Middle
Easterners, North Africans, and South Asians show low levels of high-school graduation.
The latter group shows low levels of high school graduation despite a very high level of
BA holders among the children themselves. This shows high mobility levels. The bottom,
however, belongs to Southern Europeans who present the lowest levels of parents’
education. In contrast, Western Europeans and the native populations are at the top in
terms of parents’ education. It is also interesting to note that fathers of all groups are more
educated than mothers are, except for one group: Western European women immigrants.
Mothers of these women are more educated than their fathers are. The measure of parents’
education has proven to have a significant effect on children’s education level in previous
research. Hence, both measures of mother’s and father’s education will be incorporated in
the regressions below.
Finally, descriptive statistics of mastering the local language are also highly
relevant in terms of education attainment and then integration into the labor market. In
this regards, one can see two salient exceptions. Middle Easterners and North Africans
present a significantly low percentage of those who master the local language, while
Western European immigrants are quite comparable to the numbers shown by the native
population. All other groups are in the middle, where around half of them speak the local
language as their first spoken language. Note that even the native population is not
entirely homogenous since there are some native minorities (non-immigrants) who do not
speak their country’s main language as well as specific regions where the main spoken
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language is not the official one. These findings will be incorporated in the regressions
below as well, and their influence on higher-education attainment will be tested.
Cross-Classified Multilevel Analyses of Higher-Education Attainment among
Immigrants in Western Europe
Table 3 presents five logit models of cross-classified multilevel analyses predicting
holding a BA degree. Model 1 shows the odds ratios of the effect of individual and
country variables. Model 2 keeps Model 1 variables and adds the effect of intercultural
policies, while Model 3 replaces it with the effect of targeted support policies. Then,
Model 4 examines the effect of both policies together in order to directly compare
between the two. The latter three models incorporate an interaction between the policy at
hand and the six groups under examination. This is in order to examine further the direct
effect of the two policies on each one of the groups separately. Finally, Model 5 compares
the effect of the two types of policies on the general immigrant population without such
an interaction.
All models account for both country of destination and country of origin in second
and third levels. This is to ensure that despite the relative coherence of the regions at
hand, remaining variations between countries do not affect the results. Indeed, all five
multilevel models were tested for significance over one-level analyses and found
significant. Nevertheless, one-level analyses are attached in Appendix A1. Note that the
complex structure of the models does not allow for incorporation of additional variables
such as marriage, the HDI index, and the Gini index due to the problem of over-fitting.
However, these variables were run separately and showed no significant influence on the
results. Still, these findings are available upon request as well as incorporated into the
one-level analyses in Appendix A1.
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Finally, in order to be more accurate, these cross-classified multilevel analyses
were conducted only among the ages of 25-34 who were born in a Western European
country or arrived before the age of 18. This is to ensure that they were affected by the
policies under examination. Note that sensitive analyses were run for other age ranges,
and all results are similar and available upon request from the author.
Table 3: Cross-Classified Multilevel Analyses of Having a BA among Immigrant
Children and Second-Generation Immigrants in Western Europe, Age 25-34 [about here]
Model 1 in Table 3 presents the clean effect of individual and social variables on
immigrants in Western Europe. This model serves as a benchmark in order to know the
effect of these common variables before examining the effect of particular policies. This
model shows that women are more educated than men are. In addition, high-school
graduation of parents significantly increases the chances of their children to achieve
higher education. Furthermore, there is only a weak and insignificant effect for second-
generation immigrants versus immigrants who arrived as a child. Finally, social
perceptions hardly correlate with immigrants’ achievements. As mentioned above, this
last finding might be because there are mixed mechanisms at play here and the causality
direction works in both ways. Educated immigrants are more aware of discrimination so
they report on being discriminated against in higher numbers because they can simply
identify it more easily (Nagel, 1994). This means that education increases discrimination
reporting and, thus, the two are positively correlated. On the other side, the actual
discrimination decreases the ability and motivation of immigrants to acquire higher
education and, thus, negatively correlates with education. Since the two mechanisms act
simultaneously, it is hard to draw accurate conclusions here.
21
Model 2 adds the effect of intercultural policies and examines it for each group
separately. The results show that intercultural policies significantly increase almost all
immigrant groups’ educational achievements in comparison to the reference group,
Western European immigrants. Note that the general effect of intercultural policies
(0.961) serves as a constant for the separate effect on each group. Thus, one can say that
with full intercultural policies in place (100 points in the Migrant Integration Policy
Index), Sub-Saharan Africans have 180 percent more chance to obtain a BA degree than
Africans who immigrate to a country with no such policies. Middle Easterners and North
Africans have 100 percent more, South Asians 230 percent more, and Eastern Europeans
have 300 percent more chance to graduate with a BA degree than immigrants from the
same region who live in a country with no such education policies. Only Southern
Europeans are not fundamentally different from the Western European immigrants. It
seems that both of these groups are not affected significantly by intercultural education
policies.
In order to make these results more tangible, they should be compared to the effect
of parents’ education. Education of parents is known in the literature to strongly affect
children’s education and, thus, a comparison to this variable gives a benchmark to
estimate the extent of intercultural education policies’ effect. Indeed, one can see that
high-school graduation of the father increases the chances of these immigrants to obtain a
BA degree by 174 percent, while mother’s high-school graduation increases the chances
by 118 percent. This means that the effect of intercultural education policies is
comparable to that of parents’ education.
In comparison, one can see a much lower effect when examining targeted support
policies in Model 3. Only Eastern Europeans significantly benefit from the effect of these
targeted support policies. However, even their benefit is less than their benefit from
22
intercultural education policies. Chances to obtain a BA degree by Eastern Europeans are
increased by 150 percent when such policies are fully in place. This figure should be
compared to 300 percent in regards to the effect of intercultural education policies.
In order to fully compare the effect of the two types of policies, Model 4 presents
a cross-classified multilevel analysis where both types of policies are incorporated as well
as their interaction with each immigrant group. The results show that the effect of targeted
support policies is insignificant and weak, while the effect of intercultural education
policies is higher, positive, and significant among Sub-Saharan Africans and Eastern
Europeans (among MENA and South Asians it is marginally significant at 89 and 88
percent confidence, respectively). Again, social exclusion factors show no significant
influence on educational achievements of immigrants when intercultural policies are
incorporated.
Finally, Model 5 gives the reader a broader view on the general effect of the two
types of policies on immigrants in Western Europe. This is without distinguishing
between the groups. Again, it can be seen that intercultural policies have a significant
positive effect on immigrants. An immigrant in a country with full intercultural policies in
place has 160 percent more chance of obtaining a BA degree than an immigrant who lives
in a country with no such policies. However, targeted support policies do not significantly
affect immigrants.
Discussion and Conclusions
The aim of this paper is to compare between two types of education policies towards
immigrants – intercultural and targeted support policies – and to measure their
effectiveness cross-nationally. In addition, this paper focuses on six different groups of
23
immigrant students and compares the effect of education policies on each group
separately.
The basis for this paper is the urgent need to provide policymakers in Western
Europe evidence for the effectiveness of education policies towards immigrants. In the
center of the ongoing debate about these policies lies the question of whether policies
should be targeted towards immigrants in helping them to overcome difficulties in
adapting to the new host culture, or whether policies should focus on the native
population and develop social acceptance towards immigrants that will ease their
integration. The latter are more diffuse, but this is also their advantage: they avoid
singling out immigrants while keeping costs low. Of course, support to a wider population
that includes also immigrants might be more helpful than both, but also much more
costly. Therefore, these two basic types of targeted support and intercultural policies are
under investigation here.
Fundamentally, these two components belong to two distinctly different
perspectives on the reasons for difficulties of immigrants. One perspective argues that the
source for difficulties of immigrants is their background. First- and second-generation
immigrants originating from less-developed countries have a disadvantage due to lower
levels of human capital and worse transferability of skills (Chiswick, 1978). This
perspective promotes the approach that learning new skills and adapting to the host
culture can mitigate these difficulties. Thus, special curricula and targeted support help
immigrants in overcoming difficulties and catching up with their native peers. For
example, such an experimental program was initiated in the Netherlands where after four
years of lower secondary education, a two-year middle vocational course is given to
immigrant children, in which they are prepared for the job market. A first evaluation of
24
this program (Renee van Schoonhoven et al., 2012) shows that students of this program
obtained a ‘start qualification’ four times more than the control group.
In contrast, the second perspective looks at the native population as the venue for
action if policymakers want to improve immigrants’ conditions. The argument is that
first- and second-generation immigrants are mainly affected by the majority group that
shows different degrees of social distance towards different immigrant groups. This social
distance (or alternatively a lack of social acceptance), in turn, affects immigrants and
prevents them from reaching higher achievements (Berry and Sam, 1997; Berry and Kim,
1988; Portes and Rumbaut, 2006; Shih, Pittinsky and Ambady, 1999; Steele, 1997).
When looking at the descriptive findings, one can see that Sub-Saharan Africans,
Middle Easterners, and North Africans have low rates of higher-education attainment.
However, this is also true in regards to Southern and Eastern European immigrants. Only
South Asian and Western European immigrants show strong educational achievements.
This means that proximity and citizenship do not play a central role in determining
educational attainment. More importantly, it might hint to the fact that transferability of
skills is also not so essential. This is because one can assume that Southern and Eastern
Europeans have higher transferability of skills than Sub-Saharan Africans, Middle
Easterners, and North Africans, but still findings show that they have similar educational
achievements.
These initial findings come to a test in the cross-classified multilevel analyses of
this paper. If transferability of skills is the main problem, then targeted support policies
should prove to be effective in advancing immigrants. However, if social acceptance of
immigrants is the main reason for the difficulties, then intercultural policies should prove
to be more significant.
25
Indeed, the findings of the cross-classified multilevel analyses show that
intercultural policies have a positive and significant effect on immigrants. Targeted
support policies, however, show no significant effect in advancing immigrants. To add to
that, the multilevel analyses show that having a citizenship and mastering the local
language also bear weak and insignificant effect on immigrants’ educational
achievements. This implies, again, that proximity and transferability of skills, which are
reflected in these two variables, play only a minor role if at all.
Therefore, it seems that the main issue that policymakers need to deal with is the
social exclusion of immigrants, which affect their educational achievements. As the
findings of this current paper and the literature that was brought above (e.g. Gillborn,
1997; Hermans, 2004; Kjerum, 2009; Leslie, 2003; Verkuyten and Thijs, 2002) show,
immigrant students experience high levels of discrimination. This, in turn, hurt their
ability to achieve higher (e.g. Model and Lin, 2002; Portes and Rumbaut, 2006). Hence, it
is no wonder why intercultural education policies have such a strong effect on most of the
immigrant groups under examination here, an effect that is comparable to the effect of
parents’ education1.
In checking differences between the groups in Models 2 and 4, there is not much
variance between the positive effect of intercultural policies on Sub-Saharan Africans,
Middle Easterners, North Africans, Eastern Europeans, and South Asians. Indeed, more
data is needed in order to distinguish between the groups more accurately and this is a call
for further quantitative and qualitative research. However, it is clear that Southern
Europeans and Western Europeans benefit much less from these policies than their
counterparts do. These findings correspond with the levels of discrimination and feelings
of being a minority they present in Table 2. The low levels they show in terms of social
26
exclusion mean they do not have much need for these policies and, therefore, they are less
affected by them.
Indeed, this kind of investigation was long missing from the literature due to the
recent arrival of the major waves of immigrants to Western Europe, the slow adoption of
special education policies, difficulties in measuring their consequences, and limited data.
This paper fills this gap in the literature. Nevertheless, it should be noted that more cross-
national data is needed. This need is imperative in the face of an overwhelmingly
increasing share of immigrant students in the various Western European education
systems. Future generations of immigrant students will soon enter the labor force, be
active politically, and take a stance socially. Only effective policies will ensure that they
will do this successfully, for the benefit of all.
Note
1. The reader might be concerned here about the inability to disentangle other, more
hidden, country-level factors when comparing the countries. Such hidden variables
might contribute to differences between countries and overlap the rankings (e.g.
stratified/comprehensive systems, etc.) and, thus, affect the results. However, such
hidden variables were tested and no overlapping with the rankings presented here
was found. Moreover, this current study is designed in a way that there are six
groups in each country. This means that there are actually seventy-eight points of
testing (six groups in thirteen countries). The importance of this design is that it
rules out the possibility of the hidden country-level factors in yet another way. If
the country-level factors were at play here, then all immigrants would have been
affected in the same way, after controlling for other factors such as language and
parents’ education. This is because there is no reason to assume that country-level
27
factors affect different groups in a different way. However, it is well justified to
make this assumption in regards to intercultural policies, where different groups
experience different rates of discrimination. Indeed, findings show that the further
the culture is from the Western-European culture, the stronger the effect of
intercultural policies. Furthermore, it is exactly because of such concerns that the
cross-classified multilevel analysis was applied. While policy rankings are on one
level, taking countries on a higher level serves as a measure of whether there are
differences between countries that are hidden and not reflected in the policy
rankings. However, findings show that there is not much variance between
countries after controlling for all other factors and, therefore, very little remains
unexplained on the country level.
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30
Table 1. Demographic, Social, and Economic Characteristics: Immigrant Children and Second-Generation Immigrants in Western Europe, Age
25-34
Sources: 2002, 2004, 2006, 2008, 2010, and 2012 European Social Surveys.
a Of those in labor force.
b Middle East and North Africa
Place of Origin
Gender
N
Age
Mean
%
Married
%
Citizen
Second
Generation
Unemploymenta
In Labor
Force
Feels as a
Minority
Feels
Discrimination
Sub-Saharan Africa
Female
56
29.8
41.1%
85.7%
50.0%
15.0%
89.3%
39.6%
39.3%
Male
60
29.1
43.3%
90.0%
61.7%
14.5%
86.7%
46.6%
37.3%
MENAb
Female
109
29.5
58.7%
78.0%
48.6%
18.1%
88.1%
52.8%
30.2%
Male
117
29.1
41.9%
76.1%
59.0%
17.9%
82.9%
50.9%
34.2%
South Asia
Female
31
30.2
54.8%
87.1%
51.6%
7.4%
93.5%
62.1%
16.1%
Male
28
28.3
35.7%
92.9%
46.4%
19.2%
82.1%
53.6%
21.4%
Eastern Europe
Female
119
28.5
47.0%
79.0%
38.1%
6.9%
95.0%
18.0%
20.0%
Male
131
28.5
31.5%
83.2%
48.9%
12.7%
89.3%
15.5%
13.8%
Southern Europe
Female
68
29.6
52.9%
45.6%
60.3%
6.9%
94.1%
6.0%
10.3%
Male
84
29.6
41.0%
29.8%
73.8%
6.4%
94.0%
4.8%
3.7%
Western Europe
Female
51
29.6
34.0%
58.0%
62.7%
11.6%
90.2%
7.8%
7.8%
Male
68
30
33.8%
70.6%
64.7%
5.2%
95.6%
1.5%
7.4%
Native Born
Female
7,284
29.8
38.60%
99.6%
-
6.8%
78.6%
1.7%
7.0%
Male
6,710
29.7
28.70%
99.5%
-
6.5%
91.6%
1.5%
6.1%
31
Table 2. Education Characteristics: Immigrant Children and Second-Generation
Immigrants in Western Europe, Age 25-34
Place of Origin
Gender
Holds
A BA
Degree
Average
Years of
Education
Mother
Graduated
High School
Father
Graduated
High School
Speaks
The Local
Language
Sub-Saharan Africa
Female
26.8%
13.9
22.9%
31.8%
60.7%
Male
23.3%
12.7
23.1%
25.5%
58.3%
MENAa
Female
23.9%
12.8
17.5%
26.9%
48.6%
Male
20.5%
13.4
15.4%
33.7%
32.5%
South Asia
Female
54.8%
15.3
25%
33.3%
67.7%
Male
39.3%
14.3
13.6%
27.3%
60.7%
Eastern Europe
Female
29.4%
13.9
38.3%
56.5%
57.1%
Male
23.7%
13.4
30.6%
38.0%
61.8%
Southern Europe
Female
17.6%
12.1
11.9%
18.8%
58.8%
Male
19%
12.6
13.0%
21.3%
64.3%
Western Europe
Female
43.1%
14.1
62.5%
53.8%
86.3%
Male
32.4%
14.5
50.0%
56.4%
89.7%
Native Born
Female
42.4%
14.5
50.1%
55.9%
89.1%
Male
37.6%
14.4
54.7%
60.5%
90.5%
Sources: 2002, 2004, 2006, 2008, 2010, and 2012 European Social Surveys.
a Middle East and North Africa
32
Table 3. Odds Ratios of Cross-Classified Multilevel Analyses of Having a BA among
Immigrant Children and Second-Generation Immigrants in Western Europe, Age 25-34
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Individual Characteristics
Female
1.531**
1.545**
1.494**
1.551**
1.538**
Age
1.093**
1.096***
1.097***
1.097***
1.093***
Speaks the local language
0.9
0.769
0.833
0.75
0.838
Citizenship
1.192
1.044
1.152
1.081
1.177
Born in country (2nd generation)
1.259
1.305
1.269
1.261
1.264
Father graduated high-school
2.745***
2.743***
2.677***
2.735***
2.815***
Mother graduated high-school
2.251***
2.181***
2.312***
2.290***
2.277***
Social perceptions
Feelings of discrimination
1.181
1.226
1.245
1.269
1.212
Feelings of being a minority
1.571*
1.472
1.605*
1.475
1.554*
Natives’ positive view of immigrants
1.288
1.344
1.581
1.587
1.671
Place of origina
Sub-Saharan Africa
0.039**
0.152
0.077
Middle East & North Africa
0.052**
0.168
0.105*
South Asia
0.055*
0.113
0.07
Eastern Europe
0.022***
0.042***
0.018***
Southern Europe
0.197
0.208
0.329
Intercultural policy effect
0.961**
0.968
1.016*
Interculturalism*place of origina
Sub-Saharan Africa
1.057**
1.060*
Middle East & North Africa
1.049**
1.049
South Asia
1.062**
1.06
Eastern Europe
1.069***
1.059*
Southern Europe
1.021
1.018
Targeted support effect
0.965**
0.994
0.987
Targeted support*place of origina
Sub-Saharan Africa
1.03
0.985
Middle East & North Africa
1.024
0.989
South Asia
1.053
0.997
Eastern Europe
1.050***
1.012
Southern Europe
1.02
0.993
Intercept
0.002***
0.022**
0.007***
0.008***
0.001***
SD Components
Host country SD
0.395
0.324
0.24
0.045
0.263
Origin country SD
0.814
0.684
0.710
0.706
0.827
Log likelihood
-371***
-372***
-374***
-370***
-380***
N
738
738
738
738
738
* P < .1 ** P < .05 *** P < .01
Sources: 2004, 2006, 2008, 2010, and 2012 European Social Surveys.
Note: additional covariate included in model but not shown here is ‘year of survey’.
a Omitted category: Western European immigrants.
33
Appendix Table A1. Odds Ratios of Regressions of Having a BA among Immigrant
Children and Second-Generation Immigrants in Western Europe, Age 25-34
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Individual Characteristics
Female
1.254
1.169
1.059
1.106
1.204
Age
1.095*
1.126**
1.125**
1.130**
1.109**
Marrieda
0.393***
0.393***
0.388***
0.389***
0.372***
Speaks the local language
1.201
0.883
1.058
0.98
0.903
Citizenship
0.678
0.6
0.606
0.595
0.596
Born in country (2nd generation)
1.205
1.187
1.114
1.156
1.198
Father graduated high-school
1.892**
1.919**
1.901*
1.918**
1.953**
Mother graduated high-school
2.632***
2.629***
2.877***
2.690***
2.472**
Social perceptions
Feels discrimination
1.594
1.792
1.65
1.697
1.55
Feels as a minority
1.850*
1.578
1.704
1.53
1.606
Positive attitude towards immigrants
3.024**
3.576**
3.974**
3.952**
4.641***
Host Country Characteristics
HDI indexb
0.000***
0.000***
0.000**
0.000**
0.000***
Gini indexc
1.312***
1.205**
1.208**
1.212**
1.336***
Place of origind
Sub-Saharan Africa
0.015***
0.100*
0.015***
Middle East & North Africa
0.021***
0.060**
0.022***
South Asia
0.005***
0.015***
0.004***
Eastern Europe
0.011***
0.028***
0.009***
Southern Europe
0.050**
0.148
0.044*
Intercultural policy effect
0.942***
0.960*
1.025*
Interculturalism*place of origind
Sub-Saharan Africa
1.074***
1.083***
Middle East & North Africa
1.062***
1.046*
South Asia
1.100***
1.067**
Eastern Europe
1.077***
1.052*
Southern Europe
1.047
1.051
Targeted support effect
0.950***
0.987
0.986
Targeted support*place of origind
Sub-Saharan Africa
1.042*
0.977
Middle East & North Africa
1.042**
0.983
South Asia
1.100***
1.013
Eastern Europe
1.063***
1.039
Southern Europe
1.022
1.029
Intercept
0*
0*
0*
0*
0*
Pseudo R2
0.158***
0.2***
0.2***
0.21***
0.17***
N
732
732
732
732
732
* P < .1 ** P < .05 *** P < .01
Sources: 2004, 2006, 2008, 2010, and 2012 European Social Surveys.
Note: additional covariate included in model but not shown here is ‘year of survey’.
a Omitted category: not married (widowed, divorced, single).
b Incorporated from the Human Development Index
c Incorporated from the Gini index
d Omitted category: Western European immigrants.
34
Appendix A2. Country Level Variables: Education Policies; Natives’ Public Opinion; the
Gini Index; and the HDI Index
Sources: The European Social Survey; the World Bank Databases, the UN and the
Migrant Integration Policy Index.
Notes: Attitudes towards immigrants are on a scale from 0, the most rejecting attitude, to
10, the most accepting attitude and are averaged here for all years of survey, although it is
separated for each year of survey when estimated in the analyses, for better accuracy. In
addition, scores of policies towards immigrants are on a scale from 0, the most rejecting
policies, to 100, the most accepting policies.
Country
Total N
in the
ESS
Inter-
cultural
policies
Targeted
Support
policies
Gini
index
HDI
index
Natives’
public
opiniona
Austria
3572
33
43
26
0.838
4.335
Belgium
5289
63
70
34
0.831
4.572
Denmark
4337
17
80
33.9
0.836
5.687
Finland
5958
42
90
33.7
0.827
5.450
France
4389
33
13
32.7
0.819
4.475
Germany
8080
50
30
27
0.832
4.895
Ireland
5778
33
37
24.8
0.828
5.333
Luxemburg
1365
58
47
26
0.902
5.210
Netherlands
5513
83
50
30.9
0.845
5.015
Norway
5672
83
90
25
0.884
5.151
Sweden
8118
75
90
23
0.837
6.226
Switzerland
8461
33
47
26.8
0.853
5.292
The UK
5858
92
63
28
0.834
4.450
