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Orazio Giancola, Luca Salmieri
Education and the Inclusion of Immigrants. A
Cross-National Analysis among Five European
Countries
(doi: 10.12828/90564)
Scuola democratica (ISSN 1129-731X)
Fascicolo 2, maggio-agosto 2018
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Universit`a la Sapienza di Roma (Uniroma1)
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Education and the Inclusion
of Immigrants
A Cross-National Analysis among Five European Countries
Orazio Giancola and Luca Salmieri
> OTHER TOPICS
ABSTRACT: Authors discusses outcomes from a research in 5 European countries on the
educational and labour market attainments of 1
st
and 2
nd
generation minorities. Popula-
tion of foreign origins are disadvantaged in education, in accessing labour market, and in
occupational attainments, but disadvantages are dierent and follow divergent dynam-
ics among countries in analysis. Possible explanations for dierences between countries are
reviewed, on the base of both empirical evidences and national policies for inclusion and
active citizenship.
KEYWORDS: Immigration, Educational Attainment, Labour Market, Inequalities, Social
Inclusion
Introduction
Since the Nineties immigration has become a very controversial political issue
in Europe, igniting positions and conicts in defence or in opposition to gov-
ernment policies. Debates often revolves around norms and representations of
citizenship, a dimension full of meanings and dense with crucial implications for
individual’s agency (Koopmans et al., 2005). In the sociological debate, citizen-
ship has been declined as a shifting condition and more recently as a range of
capabilities descending from and contributing to substantial and not only formal
Orazio Giancola, Dipartimento di Scienze Sociali ed Economiche, Sapienza,
orazio.giancola@uniroma1.it
Luca Salmieri, Dipartimento di Scienze Sociali ed Economiche, Sapienza,
luca.salmieri@uniroma1.it
is paper has been jointly co-authored. Authors have together and to a high degree carried out the work
cooperatively. For only formal attributional purposes, Orazio Giancola may be considered responsible for par-
agraphs 1 and 2, Luca Salmieri for Introduction and paragraphs 3 and 4.
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«agency» (Nussbaum, 2006). is recent shift has opened a more complex de-
bate on the social inclusion of immigrants: while citizenship has been described
as a «momentum concept» (Homan, 2004), sociologists have reworked the
concept in a more inclusionary direction, through multi-tiered analysis, which
pays attention to the spaces and places in which lived citizenship is practised,
that is how and at what extent social inclusion is guaranteed. Nevertheless, social
inclusion is still a complex phenomenon and therefore it is dicult to reach a
joint agreement of how it should be measured (and pursued). Since this latter is
often dened in relation to education and work (Atkinson, 1998; Raaum et al.,
2009; Fangen, 2010), we analyse substantial dimensions of social inclusion for
the 1st and 2nd generation immigrants compared to natives in 5 European coun-
tries: Italy, France, Germany, Sweden and Czech Republic. We focus on main
dimensions: access to education, educational attainments, position in the labour
market, types of employment. Our goal is to understand the inuence that each
factor may exert on the improvement of social inclusion of immigrants.
Our general hypothesis is that education generally plays a pivotal role for
social inclusivity. We expect that analysing the role of the socio-economic back-
ground in the educational and occupational outcomes of immigrants will ex-
plain inclusivity among selected countries. We also speculate that specic na-
tional welfare and integration policies frames may slow down or reverberate
the positive eect of education. Our expectation is that national dierences
generate a heterogeneity of congurations between educational systems, labour
markets and historical contexts characterized by specic traditions of inclusivity.
In sum, inclusivity should result higher or lesser pronounced in the ve selected
countries that have dierent education systems, adopt dierent labour market
policies and provide dierent welfare facilities.
Over the past decades European countries implemented dierent immi-
gration policies. ere are countries with a long-lasting legacy of immigra-
tion such as France – together with Great Britain, Netherlands and Portugal
– where immigration began already during colonialism and decolonization.
Countries such as Germany and Sweden, together with Belgium and Swit-
zerland, registered an increase of immigration paralleling the growth in the
recruitment of foreign labour force, especially during the Trente Glorieuses of
industrial and economic boom. Countries such as Italy, Spain and Greece are
experimenting a rise in immigration from the Seventies, since geographical lo-
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cations started being crucial for entering Europe by new non-EU immigrants.
At last, the Czech Republic, as a new-born country, has hosted immigration
only after entering EU (Arango, 2012).
Despite the history of immigration to Europe has been characterized by con-
tinuous population mobilities, most national frameworks have only recently
recognized the emergence of a multicultural society. Moreover, political and
welfarist approaches to social inclusion vary signicantly among national con-
texts: Germany has only recently turned its political recognition of citizenship
by the principle of jus sanguiniis to the one of jus soli, with the aim to improve
opportunities for the fully inclusion of the 2nd generation of immigrants who,
before the reform, were still legally considered foreign citizens (Mahnig and
Wimmer, 2000). France has followed a sort of opposite route, with universal-
istic policies – based on a generous granting of citizenship to non-natives and
their children (Brubaker, 2009) – now turning toward a new selective style
(Simon, 2014). Several historical changes and many shifts in policy orientation
have structured the approach of countries such as Italy – passing from a ne-
glecting approach to provisions for immigrants’ stabilization – or as Sweden –
from an extreme openness towards refugees and asylum seekers to selective and
restrictive policies (Boräng, 2015). Finally, the European landscape is further
complicated by the increasing role that historically poorly immigrated countries
– such as those of the former Eastern Warsaw Pact Block – are now playing as
a place of arrival and stabilization. e Czech Republic is an example (Okólski,
2007). We sketched up the main features of national policy attitudes toward
immigration in tab. 1.
National welfare regimes have shaped educational systems, labour markets
as well as housing and health services for natives and non-natives. Based on
the classic Esping-Andersen (1990) typology, we expect smaller or greater ed-
ucational and employment inequalities between immigrants and natives and
between 1st and 2nd generation according to each country considered: in Swe-
den, inequalities should be smaller than in other countries because of the fully
exemplication of «social democratic» welfare regime, with high social securi-
ty standards, redistributive and equitable principles operating mainly through
the education and training systems. For Germany and France, we expect more
marked inequalities, because of the historically conservative welfare regimes
which do not pursue substantial equality of status or the erosion of class ine-
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qualities (Iversen and Stephens, 2008). However, the fact that France has long
implemented assimilationist policies and maintained universalistic access to
education should make us claim a moderate egalitarianism in education and
labour market. Germany should instead conrm expectations, because it has a
more stratied educational and training system, with early selection of students
in rigid and separate tracks. Exemplifying the typical Mediterranean welfare
regime, Italy shares some features with the conservative regime, but it records
low spending levels in education and labour market policies (Agostini, 2013).
We should therefore expect a scarce erosion of inequalities between native and
TAB. 1. Historical frame of immigration in France, Germany, Sweden, Italy and Czech
Republic
COUNTRIES CATEGORIES HISTORY
France
Germany
Countries of long immigrant tradition
Inflows from ex-colonies
Inflows linked to the labour force demand
from industrial sectors
High percentage of foreign population
Immigration for economic reasons follo-
wed by large quotas of family reunifications
Currently: facilitations only for highly
skilled migrants and for specific qualified
economic sectors
Sweden
Countries of immigrant tradition
Historical openness to refugees and asylum
seekers
Advanced policies of assimilation and
legislation opened to the granting of citi-
zenship
Politicized public debate: multiculturalism
vs assimilationism
Italy
Countries of recent immigration
Inflows linked to geographical position
Inflows linked to labour demand in
informal sectors
Transition from migration to immigration
context
Countries placed at the margin of migra-
tion targets
Policies limited at coping with urgency and
recurrent regularizations
Public debate focused on borders control,
quota inflows, criminality and national
identity
Czech Republic
Countries of very recent immigration
Former communist block of eastern Euro-
pe previously closed to immigration
Transition to capitalism
Current strong migrant pressure (settle-
ment and transit)
Absence of grounded policies
Source: elaboration from authors
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non-native population, so much so that the Italian labour market is in turn
dualistic (Scherer, 2005; Garibaldi and Taddei, 2013). Since it is not possible
to typify Czech welfare regime or to characterize immigration policies because
of the too recent immigration arrivals, we do not have a starting hypothesis for
this case (Drbohlav, 2003).
1. Data and methodology
Until now, very few researches have adopted a comparative approach including
hosting countries, the whole population, educational attainments as well as po-
sitions in the labour market with those two latter as dependent variables (Levels
and Dronkers, 2008; Dronkers and De Haus, 2008; Baysu and de Valk, 2012).
We therefore examine the strength of educational and occupational careers and
the emerging dierences among age cohorts, gender, social backgrounds. We
suggest, in other words, a closer look to the aggregated transitions from school
to work for immigrants – 1st and 2nd generation – in a cross-national perspec-
tive. We then extend our analyses: cross-national comparison allows to frame
systems of education and labour markets accessibility and functioning in each
of the ve countries in relation to welfare systems, immigration policies and
social inclusion.
We used 2011 PIAAC data1 which provides details on educational attain-
ment, educational mobility, job condition and skills (estimated by standard-
ized test). Both the denition of ‘immigrant’ and the terminology adopted to
identify immigrants change from nation to nation (Bloemraad et al., 2008).
Notwithstanding these classicatory problems, we took PIAAC categories for
granted and we split out ‘natives’ – individuals born in the local national ter-
ritory by both native parents – from ‘non-natives’ – individuals born from
foreign parents – and we further cut o a ‘1st generation of non-natives’ – born
outside the country – and a ‘2nd generation of non-natives’ – born in the coun-
try, but from foreign parents.
1 Programme for International Assessment of Adult Competencies is the rst and only wave of a
large-scale survey conducted in 25 countries on adults’ prociency in processing key skills. For what is
crucial in our study, PIAAC data also contain information on occupational status, educational level and
family background of respondents.
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We merged original databases from national surveys into a unique one, in-
cluding the ve countries and we then extracted the relevant cases of popula-
tion aged 25-65 years old. We selected the following variables: i) the highest
educational level among family components used as proxy to estimate the social
background (education has been split up in ‘lower than diploma’ – lower than
secondary school – and ‘upper than diploma’ – equal or upper than secondary
school); ii) gender; iii) age, with four generational cohorts for a descriptive
analysis and two macro-cohorts for a multivariate analyses; iv) migratory back-
ground, split into ‘natives’, ‘1st generation’ and ‘2nd generation’ (the two latter
coded together as ‘non-natives’, in order to avoid loss of signicance); v) edu-
cational attainment, compared to social background, divided in three modes:
‘lower than diploma’, ‘diploma’ and ‘upper than diploma’; vi) employment sta-
tus, in three macro-modes: ‘employed or self-employed’, ‘unemployed’ and ‘not
labour forces’.
Firstly, a descriptive analysis and secondly an estimation of both variables
eects on educational attainment and on occupational status of native and
non-natives, using logistic regression, are shown. ereafter a dynamic analysis
of PIAAC data follows to assess the impact of parents’ education on individuals’
educational attainment. We implemented a set of multinomial logistics regres-
sion models: individual educational level is the dependent variable and gender
and parents’ education are the independents variables. We estimated the relative
advantage of being female rather than male and dichotomized the dierent lev-
els of education among family members. Besides, among independent variables
we also estimated the interaction eect between migratory background (‘adults
non-natives’, ‘young non-natives’ compared to ‘natives’) and age cohort. We
replicated the model for each country and then we exploited an overall model
including all the countries together.
2. Natives, non-natives and 2nd generations: the value of education
2.1. Population
Our data are consistent with studies on national migratory histories. On the
total population (tab. 2), Germany, France and Sweden have a wide share of
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individuals with migratory background, while Italy and the Czech Republic
have a much smaller one.
Education stands out as the most important mean by which an immigrant
person can succeed into the society and thus increase opportunities to get full
inclusion. Achievement of high education levels is one of the most signicant
predictors of subsequent full socio-economic integration through participa-
tion in the labour market and career advancement (Shavit and Muller, 1998;
Sullivan, 2001; Breen, 2004; Heath et al., 2008; Azzolini and Barone, 2013;
OECD, 2015).
TAB. 2. Native population or with migratory background according to age cohorts (%)
2534 3544 4554 OVER 55 TO T.
France 1st or 2nd gen. 21,0 22,4 18,4 17,4 19,0
native 79,0 77,6 81,6 82,6 81,0
Germany 1st or 2nd gen. 24,2 26,0 19,8 24,0 22,9
native 75,8 74,0 80,2 76,0 77,1
Sweden 1st or 2nd gen. 24,8 26,1 22,6 15,7 21,7
native 75,2 73,9 77,4 84,3 78,3
Italy 1st or 2nd gen. 12,3 10,6 5,8 2,4 7,9
native 87,7 89,4 94,2 97,6 92,1
Czech Republic 1st or 2nd gen. 6,3 6,9 8,0 7,5 6,3
native 93,7 93,1 92,0 92,5 93,7
Source: elaboration by authors on OECD-PIAAC data
TAB. 3. Educational attainment in France (%)
1ST GENERATION 2ND GENERATION NATIVES
25-34 ISCED II or less 23,3 12,2 10,4
ISCED III 57,3 65,5 46,5
ISCED IV or above 19,4 22,3 43,1
35-44 ISCED II or less 32,1 28,2 12,5
ISCED III 55,8 52,5 47,3
ISCED IV or above 12,1 19,3 40,2
45-54 ISCED II or less 46,7 33,9 25,3
ISCED III 45,1 50,6 51,6
ISCED IV or above 8,2 15,5 23,1
Over 55 ISCED II or less 58,4 52,9 40,6
ISCED III 35,3 38,2 42,7
ISCED IV or above 6,3 8,9 16,7
Source: elaboration by authors on OECD-PIAAC data
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We enucleated the distribution of educational qualications across age cohorts
and countries, by splitting the population between ‘native’, ‘non-native’ and ‘2nd
generation’. General results prove that inequalities among natives and non-na-
tives continue to be wide. 1st generation of migrants in France, Germany and
Sweden (tab. 3, 4 and 5) show similarities. ese countries elicit a trend of
improvement of educational attainments over time due to incoming immi-
grant ows. At the opposite, the 2nd generation show progress only in Sweden
TAB. 5. Educational attainment in Sweden (%)
1ST GENERATION 2ND GENERATION NATIVES
25-34 ISCED II or less 36,8 6,8 9,1
ISCED III 46,2 59,5 41,1
ISCED IV or above 17,0 33,7 49,8
35-44 ISCED II or less 44,7 19,0 6,4
ISCED III 45,0 54,4 47,4
ISCED IV or above 10,3 26,6 46,2
45-54 ISCED II or less 48,0 33,6 12,9
ISCED III 45,0 50,6 46,5
ISCED IV or above 7,0 15,8 40,6
Over 55 ISCED II or less 60,6 49,8 23,2
ISCED III 32,9 43,3 42,6
ISCED IV or above 6,5 6,9 34,2
Source: elaboration by authors on OECD-PIAAC data
TAB. 4. Educational attainment in Germany (%)
1ST GENERATION 2ND GENERATION NATIVES
25-34 ISCED II or less 23,5 6,9 6,7
ISCED III 41,5 57,9 44,2
ISCED IV or above 35,0 35,2 49,1
35-44 ISCED II or less 30,6 19,2 7,4
ISCED III 37,7 46,1 49,1
ISCED IV or above 31,7 34,7 43,5
45-54 ISCED II or less 27,6 30,0 18,4
ISCED III 55,8 52,5 56,3
ISCED IV or above 16,6 17,5 25,3
Over 55 ISCED II or less 55,4 48,5 34,6
ISCED III 37,1 42,2 45,7
ISCED IV or above 7,5 9,3 19,7
Source: elaboration by authors on OECD-PIAAC data
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TAB. 6. Educational attainment in Italy (%)
1ST GENERATION 2ND GENERATION NATIVES
25-34 ISCED II or less 44,1 37,6 25,0
ISCED III 49,6 50,8 48,1
ISCED IV or above 6,3 11,6 26,9
35-44 ISCED II or less 66,8 48,9 45,0
ISCED III 28,7 42,6 38,5
ISCED IV or above 4,5 8,5 16,5
45-54 ISCED II or less 70,4 64,8 56,4
ISCED III 26,3 29,8 34,4
ISCED IV or above 3,3 5,4 9,2
Over 55 ISCED II or less 78,6 86,1 70,0
ISCED III 19,3 13,9 21,8
ISCED IV or above 2,1 – 8,3
Source: elaboration by authors on OECD-PIAAC data
and Germany, whose educational system provide strong and institutionalised
non-tertiary post-secondary courses. In Italy the population with migratory
backgrounds reach poor educational attainments. Even among young people
from the 2nd generation, a large share of low educated is found (tab. 6). e
Czech Republic trend is somewhat analogous to Italy. Nonetheless, Czech out-
comes in terms of inclusion in the higher levels of the educational system for
non-natives are better than for Italy (tab. 7).
TAB. 7. Educational attainment in Czech Republic (%)
1ST GENERATION 2ND GENERATION NATIVES
25-34 ISCED II or less 31,0 22,2 4,9
ISCED III 59,7 59,4 62,6
ISCED IV or above 9,3 18,4 32,5
35-44 ISCED II or less 37,7 33,2 3,4
ISCED III 55,5 52,6 74,9
ISCED IV or above 6,9 14,2 21,7
45-54 ISCED II or less 49,8 37,4 4,7
ISCED III 46,6 51,5 75,8
ISCED IV or above 3,6 11,1 19,5
Over 55 ISCED II or less 64,4 55,1 11,7
ISCED III 32,8 34,1 70,3
ISCED IV or above 2,8 10,8 17,9
Source: elaboration by authors on OECD-PIAAC data
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France, at the opposite has a larger share of lower qualied proles among
the 2nd generation. Moreover, many young people from the 2nd generation pro-
ceeded in vocational-professional training which limits job opportunities in the
mid-high positions of the labour market.
Noteworthily younger generation of non-native population is far apart from
the natives in terms of levels of education achieved in all the ve countries. is
gap is narrower in Germany and Sweden, while it is larger in France. e gap
sounds lesser in Italy, because of the wide share of native adults and old people
with low educational attainment. Lastly, a considerable gap is also observed in
Czech Republic. All the ve countries considered, the degree of openness and
the type of educational-training system have a positive impact on the dynamics
of social inclusion.
Dronkers and Fleischmann (2007) pointed out that integration opportuni-
ties are greater when there are no signicant gaps between the levels of educa-
tion of the 2nd generation and the natives. As a matter of fact, while for the 1st
generation is plausible that integration is hampered by language barriers and
diculties in adaptation, the 2nd generation is socialized in the same native
educational system and is therefore more likely to exploit better prospects of
inclusion. We found that in countries with a more consolidated and eective
post-technical vocational system – Germany above all – 2nd generation usually
reaches higher education attainments.
2.2. Divergences and gaps in the field of education
e direct eect of parents’ education on children’s educational outcomes is an
incontrovertible fact in sociology (Boudon, 1974; Davies et al., 2002). ere
is evidence that this eect can operate with same strength both for foreign and
native students (Hustinx, 2002). e reason for good or bad school perfor-
mances of immigrant children might sometimes coincide with those of their na-
tive peers. Nonetheless, reasons strictly linked to their immigrant status might
prevail. A general eect of parental education should operate on 2nd generation
immigrants. e higher the parents’ education, the higher the children’ one. We
therefore analysed the performances of national education systems in breaking
or diminishing this correlation and found out a strong net eect of social ori-
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TAB. 8. Model of Multinomial Logistic Regression on educational attainment in 5 countries
(France, Germany, Sweden, Italy and the Czech Republic)
EDUCATIONAL
LEVEL
VARIABLES EXPB LOWER BOUND UPPER BOUND
ISCED III Female vs Male 0,961 0,96 0,962
Parents with ISCED III
or above
6,393 6,385 6,401
Non-natives adults 0,579 0,578 0,58
Non-natives youngsters 1,361 1,358 1,364
25-45 1,693 1,691 1,695
ISCED IV or above Female vs Male 1,066 1,065 1,067
Parents with ISCED III
or above
17,564 17,54 17,588
Non-natives adults 0,629 0,628 0,63
Non-natives youngsters 0,993 0,992 0,995
25-45 2,24 2,237 2,243
Note: For all the forthcoming models, reference categories are the following: for gender, ‘Male’; for parents’
education, ‘Parents with ISCED II or less’; for interaction between migratory background and age group, ‘Nati-
ve’; for the control by age, ‘46-65’
Source: elaboration by authors on OECD-PIAAC data
gin on the children probability to get upper secondary graduation. is eect
sounds even stronger considering the likelihood of obtaining university diplo-
ma and is consistent with results from Dronkers and Fleischmann (2007).
We also found out that gender variable has an eect when combined with
parental education: daughters, compared to sons, are more likely to achieve
apical degrees, while they have almost the same likelihood to obtain upper
secondary graduation. As expected, younger age groups are more likelihood
to achieve qualications higher than the upper secondary diploma. We also
found out a reduction in inequalities deriving from the interaction between age
and migration background: non-native youth (age cohorts 25-45) compared to
non-native adults (age cohorts over 45) are more likely to get a degree in higher
education.
Looking at country-by-country outcomes, substantial dierences stand out,
partly due to the national specicities generated by the interplay between immi-
grant policies and educational systems.
Dierent processes in reproducing educational inequalities are at play in
France and Germany. In the former case, family backgrounds have a strong
eect in replicating educational inequalities for people with migrant back-
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grounds. Only younger generational cohorts seem to be less aected. In the
latter case, the impact of the family background is relatively weaker, while the
eect of the migratory background resonates the one recorded for France. In
both countries, educational policies have implemented an ‘inclusive’ approach,
conrming the general inuence of a similar welfare system as well as the bene-
cial relevance of language-support policies extended not only to students, but
also to non-natives parents.
Nevertheless, if French institutions promoted high-level professional train-
ing courses only in the last two decades (Ben-David, 2017), in Germany these
pathways were historically part of the educational system (Giancola and Salmie-
ri, 2014). Finally, the French industrial and productive system has dierent his-
torical features compared to the German one: a greater relevance of State-driven
industries, a greater demand (at least until the recent past) of senior specialist
to be routed to public careers and a lesser need for technicians to be routed to
manufacturing companies. French generational inequalities are then evident by
the eect of ‘expansive’ educational policies pursued during the recent decades:
non-natives from the younger generations recover ground, at least in terms of
getting upper secondary diplomas, while reduction in inequality is statistically
negligible for tertiary educational attainments. e German tracks system of
education erodes inequalities with respect to the opportunities to obtain higher
TAB. 9. Model of Multinomial Logistic Regression on educational attainment in France
EDUCATIONAL
LEVEL
VARIABLES EXPB LOWER BOUND UPPER BOUND
ISCED III Female vs Male 1,074 1,073 1,076
Parents with ISCED III
or above
1,844 1,84 1,848
Non-natives adults 0,257 0,256 0,258
Non-natives youngsters 0,845 0,842 0,849
25-45 1,844 1,837 1,852
ISCED IV or above Female vs Male 1,095 1,092 1,097
Parents with ISCED III
or above
6,151 6,107 6,196
Non-natives adults 0,252 0,251 0,253
Non-natives youngsters 0,41 0,408 0,411
25-45 3,163 3,148 3,178
Source: elaboration by authors on OECD-PIAAC data
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qualications, while the universalistic French system intensies inequalities: the
net eect of the type of educational system is greater than the net eect of the
type of welfare regime (tab. 9 and 10) (Hanushek and Wößmann, 2006).
Logistic regression for Sweden shows dierent outcomes (tab. 11): non-na-
tives can benet from Swedish educational upward mobility almost as much as
TAB. 10. Model of Multinomial Logistic Regression on educational attainment in Ger-
many
EDUCATIONAL
LEVEL
VARIABLES EXPB LOWER BOUND UPPER BOUND
ISCED III Female vs Male 1,72 1,718 1,721
Parents with ISCED III
or above
2,876 2,868 2,883
Non-natives adults 0,475 0,473 0,477
Non-natives youngsters 0,837 0,835 0,838
25-45 1,828 1,825 1,831
ISCED IV or above Female vs Male 1,833 1,831 1,835
Parents with ISCED III
or above
7,297 7,275 7,319
Non-natives adults 0,355 0,354 0,356
Non-natives youngsters 0,422 0,42 0,423
25-45 2,135 2,131 2,138
Source: elaboration by authors on OECD-PIAAC data
TAB. 11. Model of Multinomial Logistic Regression on educational attainment in Sweden
EDUCATIONAL
LEVEL
VARIABLES EXPB LOWER BOUND UPPER BOUND
ISCED III Female vs Male 1,296 1,288 1,303
Parents with ISCED III
or above
2,358 2,341 2,375
Non-natives adults 0,302 0,299 0,305
Non-natives youngsters 0,714 0,707 0,721
25-45 2,044 2,029 2,06
ISCED IV or above Female vs Male 1,769 1,758 1,779
Parents with ISCED III
or above
5,351 5,338 5,365
Non-natives adults 0,316 0,313 0,319
Non-natives youngsters 0,644 0,638 0,65
25-45 2,132 2,116 2,149
Source: elaboration by authors on OECD-PIAAC data
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natives do. Family backgrounds have a less pronounced eect and the recovery of
2nd generations is consistent. Over the time, the Swedish educational and training
system has tackled down inequalities between natives and non-natives. Togeth-
er with long-term social-democratic welfare, it has contributed in fostering the
conditions for contrasting the eect of social classes and socio-economic back-
grounds. Furthermore, in Sweden, as in Germany, being a woman increases the
likelihood to achieve tertiary education. It is obvious that the history of migration
toward Sweden diers substantially from those toward France and Germany. It is
a case on its own (Valenta and Bunar, 2010; Brochmann and Hagelund, 2012).
In Italy multiple and pronounced inequalities stem out from socio-econom-
ic backgrounds (Venturini and Villosio, 2008; Giancola, 2010; Ballarino et al.,
2016), that is predominantly factual for non-natives who are disadvantaged for
the eect of both parental education and migratory background. Even genera-
tional recovery is rather scanty: the likelihood of young non-natives to achieve
tertiary education degree is far less pronounced than their native peers (tab.
12). Low educated migrants from the 1st generation are trapped into low skilled
(often irregular) jobs and this reverberates in the second-generation, in terms
of both primary eects (socialization, local inclusiveness, discriminations) and
secondary eects (ambitions and educational choices). In Italy the disadvantage
of non-natives is the highest among the countries considered.
TAB. 12. Model of Multinomial Logistic Regression on educational attainment in Italy
EDUCATIONAL
LEVEL
VARIABLES EXPB LOWER BOUND UPPER BOUND
ISCED III Female vs Male 1,131 1,129 1,133
Parents with ISCED III
or above
7,053 7,032 7,074
Non-natives adults 0,319 0,309 0,329
Non-natives youngsters 0,658 0,655 0,661
25-45 2,266 2,263 2,27
ISCED IV or above Female vs Male 1,688 1,684 1,692
Parents with ISCED III
or above
14,795 14,716 14,875
Non-natives adults 0,317 0,315 0,319
Non-natives youngsters 0,414 0,403 0,422
25-45 2,901 2,894 2,909
Source: elaboration by authors on OECD-PIAAC data
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Non-natives are strongly disadvantaged in the Czech Republic too. Yet, the im-
pact of family background on children likelihood to attain higher education
diplomas is contained. Inequalities between young and old non-natives is re-
duced. It seems that while the proportion of both 1st and 2nd generation of
non-natives is rather low (tab. 2), Czech welfare policies and educational system
have had little inuence on immigrants’ opportunities to be socially integrated
(Adámek, 2012).
3. Different degrees of inclusivity among national labour markets
Immigrants inclusion in national labour markets depends on many factors, in-
cluding structural features of the productive system, active and passive work
policies, universalistic or selective social policies (Angrist and Kugler, 2003).
Such interactions display specic degrees of inclusion and aect the match-
ing between immigration history and immigrants’ expectations, also accord-
ing to economic cycles. We found similarities and dierences among the ve
countries. Assonances concern the gender variable: a systematic disadvantage of
women is found in each country, with the greatest inequality in Italy and Czech
TAB. 13. Model of Multinomial Logistic Regression on educational attainment in Czech
Republic
EDUCATIONAL
LEVEL
VARIABLES EXPB LOWER BOUND UPPER BOUND
ISCED III Female vs Male 1,367 1,364 1,37
Parents with ISCED III
or above
1,713 1,699 1,727
Non-natives adults 0,363 0,358 0,367
Non-natives youngsters 0,444 0,425 0,464
25-45 1,551 1,539 1,563
ISCED IV or above Female vs Male 1,453 1,45 1,457
Parents with ISCED III
or above
5,775 5,712 5,84
Non-natives adults 0,376 0,369 0,382
Non-natives youngsters 0,449 0,441 0,457
25-45 2,212 2,194 2,23
Source: elaboration by authors on OECD-PIAAC data
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Republic, the lowest in Sweden and France. Germany has an intermediate po-
sition. Similarly, in all ve cases, family educational background stands out as a
strong discriminating factor in the likelihood of getting a job. Only in Sweden
the eect has a lower magnitude (tab. 14).
Further, having a 5-year high school diploma is rewarding for immigrants
in each case, but especially in Germany, where job opportunities for 1st and 2nd
generations are commonly available after secondary education and non-tertiary
post-secondary courses. Finally, the share of employed natives is higher than
that of non-natives in each country. Being of foreign origins continue to be an
adverse mark when accessing or maintaining employment. e eect of the
migratory background, however, is attenuated in France, Germany and Sweden,
probably because of the longer historical presence of immigration. Substantial
dierences are on the contrary recorded for those who obtained degrees in ter-
tiary education: in France and in Italy, tertiary graduates from the 1st and the 2nd
generation have higher chances of being employed. Less chances are recorded
for Germany. e eect of tertiary education is even less robust in Sweden and
Czech Republic.
In general, young foreigners are less penalized than adults and the elderly,
since integration processes are diachronically improving: in France and Ger-
many we found out an improvement for young foreigners, but at the same
time, inequalities in the labour market related to the migration background are
persisting.
Outcomes on the inclusion in the labour market allow us to make some gen-
eral inferences. Germany with an immigration history of employed guest-work-
TAB. 14. Probability of being employed vs unemployed (binomial logistic models)
FEMALE
VS
MALE
PARENTS
WITH
ISCED II
OR LESS
ISCED
III
ISCED
IV OR
ABOVE
NON
NATIVE
ADULTS
NON
NATIVE
YOUTH
2544
VS
4565
France 0,490 2,008 2,350 4,675 0,477 0,704 0,889
Germany 0,425 1,430 2,876 3,648 0,515 0,773 0,919
Sweden 0,733 1,292 2,358 2,784 0, 588 0,986 1,066
Italy 0,210 1,617 2,053 5,918 0, 378 0,591 0,810
Czech Republic 0,241 1,799 1,713 2,310 0,242 1,027 1,224
Source: elaboration by authors on OECD-PIAAC data
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ers and Sweden with economically motivated (refugees) migrants who settled
with their families in large numbers, have pushed state agencies to develop in-
tegration policies which have been reducing inequalities over the time. Even if
those two countries must face the ageing of the 1st generation and the dicult
challenge to ght recent marginalisation of 2nd and 3rd generations, their labour
markets still employ high numbers of non-natives thanks to a performative ed-
ucational and training system. Italian immigration experience has been charac-
terised by the absence of consistent ad-hoc policies, leading to a high number of
immigrants living in the country unocially or without proper documentation.
e irregular or illegal status so widespread among the foreign population has
implications not only for their employment status, but also for their eective
social integration. e lack of active policies directed at non-native population
exacerbates the inequalities of origins which the local educational system is not
able to mitigate. e employment performance of adult foreigners compared
to those of younger generations is explained by the historical problem of youth
unemployment, a topic feature of the Italian labour market. Furthermore, in-
equalities in the Italian labour market are deeply marked by gender gaps, pos-
ing immigrated women in a triple disadvantage: they are worse penalized than
Italian women; they must balance work and family needs without relying on
public services for care activities; they have to compete in restricted segments of
the informal labour market as caregivers, domestic workers, waitresses, cleaners.
In Czech Republic, employment opportunities for foreigners, both young and
adult, are far less than those for natives. However, the Czech very small share
of population of foreign origins in older age benets for better employment
conditions than the younger immigrants.
4. Concluding remarks: inclusiveness
vs
segmentation
Before briey sketching out the complex ndings coming from cross-national
comparison, a series of cautions must be signalled: there are many aspects, oth-
ers than educational and economic factors, aecting social inclusion of foreign
population (Levels and Dronkers, 2008M Heath et al., 2008; Baysu and Valk,
2012). Culture, religion, openness towards the society and local acceptance of
multiculturalism, all have strong inuences on the inclusion of immigrants.
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Additionally, the dierent composition of non-natives ethnical origins may in-
uence the overall degree of inclusion in each destination country (Dronkers
and Fleishmann, 2007): high percentages of immigrants from certain linguistic
areas may increases diculties in learning the language of the hosting country
and in turns prevent wider quota of non-natives students to reach the same
educational level of their native peers (Gang and Zimmerman, 2000). At pres-
ent, students who speak at home the language of education get on average a 30
points greater score at PISA test (Giannetti and Mariani, 2017). It is equally
important to remark that labour market segmentation aects specic ethnic
groups and has a territorial cleavage within each national context too. Also,
naturalization policies can exert a powerful inuence on the reduction of edu-
cational and employment inequalities: the more generous a policy of granting
formal citizenship is, the more opportunities for studying and advancing a 2nd
generation has.
Being unsustainable to consider all possible explanatory variables, we focused
on the main ones related to country-context system of social inclusion – i.e.
education and labour market – thus leaving apart (ethnic, religious linguistic)
variables related to the immigrant population, except for their socio-economic
background which is crucial. Our study has therefore observed how educational
systems and labour markets impact on social inclusion. Instead of consider-
ing a specic cohort age-group, or a specic population in a given moment
of its life-cycle, we run out statistical exercises on the whole adult population
(aged 25 to 65) in order to grasp a full-covering dynamic of social inclusion,
analysing natives vs non-natives and, among those latter, 1st generation vs 2nd
generation. Multinomial models, as well as binomial models, made the migra-
tion background interact with age (dichotomized in two macro cohorts, 25-45
and 46-65). e main evidence is that all ve countries considered, younger
generations of migrant origin achieve less than natives in terms of educational
and occupational attainment. Nonetheless, we can draw a cross-national map
of social inclusivity: the Swedish context reduces inequalities in the origin for
the 2nd generation of non-natives, rstly and largely in the achievement of high-
er education and secondly, albeit less incisively, in occupational attainments.
Swedish integration programmes are characterised by an increasing number of
labour market related integration measures. In Sweden the main objective of
integration measures is to promote the socio-economic inclusion and independ-
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ence of immigrants within the context of a society based on the principle of
diversity: Sweden is more on multiculturalism, whereas in other countries poli-
cies are more assimilationist. Full covering of nursery and pre-primary schools’
services, improving language skills, labour market measures such as work place-
ment and job subsidies, liberal family reunication rules, relatively easy access
to long-term residence status and citizenship (Brännström, 2015) are no doubts
explanatory factors in assessing Swedish as the most inclusive country among
the ve we considered.
German three-track educational system sustains positive degrees of inclu-
sion for non-natives. Nationwide reforms have been taken up by the Federal
government concerning immigrant integration through a National Action Plan
(NAP) including sustainable inclusion through education for youth with a mi-
grant background. Learning the German language is compulsory for minority
groups and parents are encouraged to do the same to be more involved in their
children’s education in cooperation and with assistance from schools and pa-
rental councils. Despite crucial dierences among Länder educational systems
and despite the early selective educational tracks (or just for this latter reason),
German youngsters with migrant background have the widest chances to get a
job when compared to their peers in Sweden, France, Italy and Czech Republic.
Nonetheless, positive inclusion via early employment has its reverse side of the
coin in the very low share of upper qualied positions held by them. Even if
more disadvantaged native and immigrant parents are aspiring for their children
to get higher education – the Gymnasium and then university – there are still
gaps between students due to socio-economic background. Germany’s Voca-
tional Education and Training sector is the major channel for the integration of
a growing number of students with a migrant background. Self-selection and
discrimination are here both at play. Students mainly of immigrant background
seeking to further their qualications through training, totalling at more than
250.000 in 2013, ended up in transitional facilities, a system supposed to assist
in preparing students for the change from education to training (Federal Min-
istry of Education and Research, 2014).
e French educational system guarantees equal access for all – except for
tertiary qualications where non-natives and 2nd generation have scarce chances
to enter prestigious universities – but job opportunities for immigrants and
children of the 2nd generation heavily depend from parents’ social class.
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Ambivalent processes are at play in Czech Republic: on one hand, the weight
of social origins on non-natives employment opportunities is very strong, on
the other the younger generation of foreign origins relies on wider employment
opportunities than the older generation do. As an ex-socialist, late independent,
new EU member and recent immigration country, Czech Republic has a weak
and unstructured welfare system from which rare and isolated intervention of
active labour policy are emerging.
e Italian case shows some dierences since inclusion of students with mi-
gration background is strongly eective in primary education, rather than in
further educational steps. At lower education levels, inequalities between 1st and
2nd generations have been decreasing over the last decades. But when consider-
ing higher levels of education attainment, intergenerational mobility has not yet
been reached and inequalities between natives and non-natives are wider. Being
Italy a new destination country and only lately a settlement country, integration
policies in upper-secondary education have not been yet a priority of the politi-
cal agenda. Once at upper secondary level, students with migratory background
are almost systematically oriented towards 5-years long professional and techni-
cal or 3-year professional tracks (Azzolini, Barone, 2012) where they risk early
drop-out and social segregation. Summing up, a segmented (and dierentiated)
approach prevails in the Italian secondary school system where no inclusion
policy supports vertical continuity along school paths. Italian new eorts to
tackle inequalities deriving from socio-economic and migration backgrounds
should encompass inclusive policies in upper secondary schools, by recognizing
the needs of students from 1st and 2nd generation.
Institutional sources (Eurydice, 2009; European Commission, 2016) and
international empirical researches prove that univocal models of inclusion are
hard to be nd out (Osadan and Reid, 2016). Nonetheless, general ndings
from our study prove that language learning at school is a key-factor for the in-
tegration of students but also of their families. Sweden, Germany and at a lesser
degree France, developed and consolidated successful policies aiming at linguis-
tic inclusion in primary and lower secondary education. Selection policies that
support high-skilled migration – as recently in Sweden and France – also have
ambiguous results: from one side they promote rapid inclusion for the newcom-
ers; from the other they produce striking inequalities hitting 2nd generation with
a lower socio-economic background.
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Universalistic and full-time provision of nursery and pre-primary schools
positively stimulates pupils and the benets reverberate in the long run as well.
at is the case for Sweden and France. De-segregation policies allowing adoles-
cents with an immigrant background to stay in class with their native peers are a
tool of social inclusion too. Germany and Italy seem to have learned this lesson.
Impacts on social stratication and therefore inclusion may be produced by
the educational tracking system: if early tracking may encourage early matching
with labour market needs, enhancing employment of non-natives and students
from the 2nd generation as the German case elicits, conversely the resulting wid-
ening of inequalities in higher education may hamper social inclusion, reduce
intergenerational mobility and prevent immigrants in achieving the same edu-
cational results of natives.
Pathways of inclusion in the indigenous society tend to be heterogeneous.
ey change in relation to a whole range of factors: from living conditions to
material, relational and symbolic resources. Such resources evolve and may be
‘capitalized’ along educational and occupational careers and when combined to
higher educational attainments, may be crucial to access to further resources
such as jobs, wages, professional networks, housing, civic activities, political
involvement. Since the inclusion/exclusion polarity is therefore conditioned by
a wider range of factors – such as specic ethnicity or religious aliation, the
length of time spent in the host country, housing, health, how the free time is
spent, participation to cultural and civic activities etc. –, inclusion outcomes
from social background, education and occupational attainments are a key trace
for future research tracks.
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