Immigrant background and expected early school leaving in Europe : evidence from PISA.
Date: 2018-02-06
Institutions: Joint Research Centre
Subjects: child of migrant, dropout, education policy, educational system, immigration, pupil, research report, right to education

Immigrant background and 
expected early school leaving in 
Europe: evidence from PISA 

Hippe, Ralph 

Jakubowski, Maciej

 2018 

EUR 28866 EN

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

Contact information  
Name: Ralph Hippe 
Email: ralph.hippe@ec.europa.eu 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC109065 

EUR 28866 EN 

PDF 

ISBN 978-92-79-76328-1 

ISSN 1831-9424 

doi:10.2760/111445 

Luxembourg: Publications Office of the European Union, 2018 

© European Union, 2018 

The reuse of the document is authorised, provided the source is acknowledged and the original meaning or 
message of the texts are not distorted. The European Commission shall not be held liable for any consequences 
stemming from the reuse. 

How to cite this report: Hippe, R. & Jakubowski, M. (2018). Immigrant background and expected early school 
leaving in Europe: evidence from PISA; EUR 28866 EN; Publications Office of the European Union; 
Luxembourg; ISBN 978-92-79-76328-1; doi:10.2760/111445; JRC109065

All images © European Union 2018, except: cover page, source: Thinkstock/Monkey Business; background 
maps: © EuroGeographics for the administrative boundaries. 

Contents 

Note ................................................................................................................... iii 

Executive summary .............................................................................................. iv 

1  Introduction ...................................................................................................... 1 

2  Literature review ............................................................................................... 3 

2.1

  Immigrant students in Europe ....................................................................... 3 

2.2

  Early school leaving and educational expectations ............................................ 4 

3  Data and methodology ....................................................................................... 6 

3.1

  Variables included in this study ..................................................................... 6 

3.2

  Sample sizes ............................................................................................. 10 

3.3

  Econometric model .................................................................................... 11 

4  Empirical analysis ............................................................................................ 12 

4.1

  Descriptives .............................................................................................. 12 

4.1.1  Characteristics of students with immigration background ........................ 12 

4.1.2  The intersection of immigrants and educational expectations ................... 19 

4.1.3  Summary descriptives of variables ....................................................... 26 

4.2

  Multilevel regression analysis ...................................................................... 27 

4.2.1  For the entire EU ............................................................................... 27 

4.2.2  Country by country ............................................................................ 30 

4.2.3  Distinction among EU and non-EU immigrants ....................................... 31 

5  Conclusions .................................................................................................... 31 

References ......................................................................................................... 34 

List of abbreviations and definitions ....................................................................... 39 

List of figures ...................................................................................................... 41 

List of tables ....................................................................................................... 42 

Appendix ............................................................................................................ 43 

Annex 1. Europe 2020 targets for early school leaving ......................................... 43 

Annex 2. Compulsory schooling in Europe ........................................................... 47 

Annex 3. Definitions of immigrant background .................................................... 48 

Annex 4. Recent immigration policies in MS ........................................................ 49 

Annex 5. Background on PISA ........................................................................... 51 

Annex 6. Missing data imputation ...................................................................... 52 

Annex 7. Details on the questions asked in PISA to construct the variables ............. 53 

Annex 8. Additional descriptive tables and figures ................................................ 56 

Annex 9. Multilevel regressions, country-by-country ............................................ 61 

i 

Annex 10. Multilevel regressions, distinction among EU and non-EU migrants ......... 69 

ii 

 
 
Note 

This  report  is  part  of  the  CRELL  IX  Administrative  Agreement  agreed  between  DG 
EDUCATION AND CULTURE (EAC) and DG JOINT RESEARCH CENTRE (JRC). Its content is 
stipulated under point 2.2 of the Technical Annex accompanying CRELL IX.  

Acknowledgements 

The  authors  would  like  to  thank  colleagues  from  DG  JRC  and  DG  EAC  for  their  useful 
comments on earlier versions of this report.  

Authors 

Hippe, Ralph (European Commission, JRC, Italy) 
Jakubowski, Maciej (Warsaw University and Evidence Institute, Poland) 

iii 

Executive summary 

This  technical  brief  analyses  the  relationship  between  immigrant  status  and  educational 
expectations  in  PISA.  Migration  flows  from  outside  and  within  the  EU  have  increased  in 
recent  years,  and  this  has  raised  the  attention  of policy  makers  and  the  general  public, 
with special interests on the implications that those flows can have on, among other, the 
education system and the labour markets. At the same time, the EU has set the Europe 
2020 headline target of reducing the share of early school leavers to 10 % within the EU. 
Early  school  leavers  become  generally  disadvantaged  socially  and  economically  in  later 
stages  in  life,  so  that  it  is  important  to  better  understand  the  motivations  for  leaving 
school  and  provide  adequate  policy  solutions.  The  European  Commission  (2016,  p.  3) 
indicates  that  early  school  leavers  are  more  likely  to  come  from  immigrant  student 
groups,  as  their  “early  school  leaving  rates  are  nearly  twice  as  high  as  for  the  native 
population”.  Yet  it  also  emphasises  that  there  is  still  a  lack  of  evidence  pointing  to  the 
underlying  reasons.  In  particular,  it  is  not  clear  whether,  among  early  school  leavers, 
immigrants  students  are  more  frequent  due  to  specific  reasons  related  to  the  status  of 
immigrants  or  whether  they  are  more  frequent  because  immigrant  students  are  more 
likely  to  possess  the  set  of  characteristics  that  are  normally  associated  to  early  school 
leaving behaviour (such as belonging to low socio economic status). 

This study analyses the factors that are most strongly related to the probability to leave 
school early, putting special attention to immigrant status (by differentiating among first 
and  second  generation  immigrants  and,  where  possible,  among  EU  and  non-EU 
immigrants). To this end, we use OECD’s PISA data, which are the most widely employed 
data  on  international  student  assessment.  Since  early  school  leavers  cannot  directly  be 
considered  with  these  data,  we  focus  on  educational  expectations,  including  the 
expectation  to  dropout  early  from  school.  As  the  related  literature  emphasises,  these 
expectations  are  very  closely  linked  to  actually  realised  educational  career  patterns. 
Therefore,  we  can  use  expectations  to  gain  insights  on  the  factors  influencing  early 
school leaving. In addition, we also employ data from Eurostat to complement the picture 
on early school leavers and immigrants. 

First,  we  provide  a  range  of  descriptive  data  on  immigrants  and  expected  early  school 
leavers. Second, we run a number of two-level logit regression models, including a range 
of  student-  and  school-level  variables.  In  particular,  we  consider  all  (available)  EU 
Member  States  together,  before  providing  results  for  each  MS  individually.  Finally,  we 
also  distinguish  more  specifically  between  EU  and  non-EU  immigrants  in  our  regression 
models. 

The  results  show  that,  when  controlling  for  individual  and  school  characteristics, 
immigrant  students  do  not  structurally  differ  in  their  expected  early  dropout  probability 
from  natives  across  Europe.  In  other  words,  the  reasons  why  students  expect  to  leave 
school early are the same for both immigrant students and natives.  This finding implies 
that  it  is  more  important  to  focus  on  the  common  factors  that  are  associated  with 
expected  early  school  leaving.  In  particular,  our  results  suggest  that  these  are,  at  the 
students’ level, the socio-economic background of students, their epistemological beliefs 
and grade repetition, while, at the school level, the most consistent factor is the school’s 
mean expected early school leavers rate. The school-environment thus appears to play a 
key  role  in  shaping  educational  expectations.  Among  the  student-related  factors,  grade 
repetition  is  the  most  amenable  by  policy,  so  that  grade  repetition  practices  may  be 
reconsidered by national policy makers.  

iv 

 
1  Introduction 

This  brief  pertains  to  EAC’s  strategic  area  of  Fighting  inequalities,  promoting  social 
inclusion and investment in young people, common EU values and Solidarity Corps, and 
in  particular  addresses  the  need  to  better  understand  the  educational  expectations  of 
both first and second generation immigrants, in an attempt to obtain greater insights on 
the increased risk of early dropouts for this group of individuals. It is also related to the 
CRELL IX Technical Report on Civic and Citizenship Education. 

Achieving equity and improving educational levels of young people is a policy priority for 
the EU.1 As a consequence, the Europe 2020 headline targets in education sets as goals 
that  at least  40 %  of  30-34  year-olds  complete  tertiary  education  and  that  the  rates  of 
early  school  leaving  are  reduced  to  below  10  %  in  the  EU28.  Recently,  the  European 
Commission has also suggested to lower the latter benchmark for early school leaving to 
5 % by 2025 (European Commission, 2017). Early school leaving is a crucial issue as it 
“is  an  obstacle  to  economic  growth  and  employment.  It  hampers  productivity  and 
competitiveness,  and    fuels  poverty  and  social  exclusion”  (European  Commission,  2016, 
p. 1). However, early school leaver (ESL) rates vary substantially among Member States 
(MS)  (see  Figure  1).  In  the  EU,  Malta  has  the  highest  ESL  rate  (20 %)  in  2016,  while 
Croatia has the lowest one (3 %). 

Figure 1. Shares of ESL in Europe, 2016  

19 - 34
11 - 19
10 - 11
8 - 10
7 - 8
5 - 7
3 - 5

Note:  7  classes  automatically  defined.  Shares  given  in  percent.  Only  those  European  (i.e.,  EU  and  non-EU) 

countries for which Eurostat provides data are shown in the map. 

Source: Eurostat (2017b). Background map: © EuroGeographics for the administrative boundaries. 

Early  school  leaving  is  a  complex  issue  and  a  concern  for  policy  makers  as  empirical 
evidence  also  shows  that  dropping  out  from  school  is  connected  with  several  forms  of 
individual  and  social  disadvantages,  with  significant  negative  consequences  for  job, 
wages  and  life  satisfaction  (e.g.,  European  Commission,  EACEA,  Eurydice,  &  Cedefop, 
2014).  In  particular,  immigrant  students  “are  largely  over-represented  among  the  early 
leavers from education and training in many European countries” (European Commission 
et al., 2014, p. 38). 

Persisting  disparities  between  immigrants2  and  natives  in  educational  (and  labour 
market) outcomes in MS are a source of inequality and social exclusion (see Flisi, Meroni, 

1  For  more  information  on  equity  in  education  related  issues,  see  Hippe,  Araújo,  &  Dinis  da  Costa  (Hippe, 

Araújo, & Dinis da Costa, 2016). 

2 In this brief, the terms immigrants and migrants are used synonymously. 

1 

 
 
 
                                           
& Vera-Toscano, 2016; Rodrigues, 2018). Therefore, comparing educational expectations 
between  immigrants  and  natives,  at  an  early  age,  is  particularly  important  if  policy 
makers intend to counteract disparities in educational achievement that later in life lead 
to social and economic inequalities. 

The  recent  wave  of  large-scale  refugee  intake  by  MS  has  increased  the  challenges 
associated  with  the  integration  of  immigrants  in  the  host  countries  (European 
Commission,  2016).  In  this  context,  education  is  key  to  successful  integration  and 
employability  (OECD,  2016c).  At  the  same  time,  the  free  movement  of  persons  among 
MS has led to significant worker and student mobility within the EU. In consequence, it is 
useful to distinguish immigrants not only by their time of arrival in a MS (first or second 
generation)  but  also  by  their  place  of  origin  (we  distinguish  between  EU-  or  non-EU 
origin).   

Our  objective  is  to  better  understand  inequalities  in  (expected)  early  school  leaving 
within and across MS. In addition, we analyse the factors that are most strongly related 
to  disparities  in  the  probability  to  leave  school  early,  putting  special  attention  to 
immigrant status (by differentiating among first and second generation immigrants and, 
where possible, among EU and non-EU immigrants).  

To achieve this task, this technical brief uses data from OECD’s Program for International 
Student  Assessment  (PISA).3  As  PISA  2015  includes  a  broader  set  of  variables  than 
previous  rounds,  we  are  able  to  consider  a  large  range  of  possible  relevant  factors  that 
may  be  related  to  early  school  leaving  (and  immigrant  status).  However,  ESL  rates 
cannot be measured directly in international student assessment data like PISA. Thus, it 
is  only  possible  to  consider  students’  expectations.  Still,  expected  educational  levels 
provide  useful  information  on  potential  future  educational  attainment.  In  fact,  the 
relationship  between  expectations  and  (later)  achievement  is  well  established  in  the 
social  sciences  (Portes  et  al.  2010).  Therefore,  we  can  use  these  expectations  to  gain 
insights  on  the  factors  influencing  early  school  leaving.  Furthermore,  we  also  use 
complementary data from Eurostat on migrant populations in Europe. 

While  PISA  2015  includes  data  on  students  with  immigrant  background  in  all  MS,  for 
some MS the number of migrants in the sample is too low and so we cannot include them 
in  our  analysis.  In  addition,  for  a  few  MS  there  is  no  information  on  educational 
expectations  of  students  in  PISA,  which  further  reduces  the  sample.  For  these  reasons, 
we  analyse  the  expected  educational  level  in  accordance  with  these  sample  size 
limitations.   

We analyse educational expectations first by computing descriptive statistics and then by 
running  a  range  of  regression  models  for  all  MS  together  and  for  each  MS  separately. 
More specifically, we use two-level logit regression models that include both the student 
and  the  school  level  to  explore  the  factors  that  are  significantly  correlated  with  the 
probability to expect leaving school early. In a final step, we also distinguish among EU 
and  non-EU  immigrants  in  further  regression  models,  by  comparing  each  group  to 
natives and by comparing directly both groups to each other. 

The  results  for  all  MS  together  indicate  that  there  are  no  inherent  differences  between 
natives and immigrants in Europe in the factors that influence the probability of expected 
early school leaving. This finding implies that instead of focussing on immigrant-specific 
measures,  it  may  be  more  appropriate  to  consider  policy  measures  that  deal  with  the 
underlying  issues  leading  to  expecting  early  school  leaving  that  are  common  to  both 
natives  and  immigrant  students.  In  fact,  our  regressions  show  that  these  underlying 
factors  are  both  related  to  students  and  to  schools.  At  the  student  level,  the  most 
relevant  factors  are  socio-economic  background  of  students,  epistemological  beliefs  and 
grade  repetition.  At  the  school  level,  it  is  the  school’s  mean  expected  early  school 
leavers’ rate.  

3  PISA  data  are  described  more  in  detail  in  section  3.  For  more  information  see  also  the  official  OECD  PISA 

website: http://www.oecd.org/pisa/. 

2 

These  findings  are  largely  confirmed  by  our  country-by-country  multilevel  regressions. 
Interestingly, the only significant factor across all MS is the school’s mean expected early 
school  leavers  rate,  confirming  that  the  school-environment  plays  a  crucial  role  in 
shaping educational expectations. All other factors, both at the student and school level, 
are  to  varying  degrees  consistent  across  MS,  but  the  student-related  factors  that  are 
mostly  relevant  in  the  regressions  for  the  entire  EU  are  also  mostly  important  in  the 
country-by-country design. The detailed results for each MS and the distinction between 
EU  and  non-EU  immigrants  show  some  specific  features  that  are  characteristic  of  each 
MS’s education system and immigrant population, which may provide useful information 
for MS.  

The  technical  brief  is  divided  as  follows.  First,  we  review  the  literature  on  immigrant 
student  populations  and  educational  expectations.  The  data  and  methodology  are 
presented in the following section, which is followed by the empirical analysis. Finally, the 
conclusion  sums  up  the  results  of  the  technical  brief,  provides  policy  recommendations 
and indicates future research avenues.  

2  Literature review 

2.1

  Immigrant students in Europe 

The  integration  of  immigrants  across  European  educational  systems  is  a  priority  for 
policymakers in the European Union (Hippe et al., 2016).4 Similarly, the OECD (2016c, p. 
243)  stresses  that  “[m]igration  puts  enormous  strains  on  both  host  communities  and 
immigrants themselves; but it can also provide new opportunities for countries that face 
ageing native-born populations and the threat of labour and skill shortages”. In general, 
the educational achievement of immigrants lags behind that of native students in almost 
all MS. In most MS immigrant students come from less favourable backgrounds as their 
parents  have,  on  average,  lower  levels  of  education  than  native  individuals.  In  fact, 
evidence  shows  that  socio-economic  status  or  family  background  are  more  strongly 
associated  to  students’  performance  than  immigrant  background  (Blanden  &  McNally, 
2015;  Dustmann,  Frattini,  &  Lanzara,  2012;  OECD,  2016c;  Schnepf,  2007).  Clearly,  it 
should be a priority for policy makers to support and raise achievement of low performing 
students,  namely,  students  with  socio-economically  disadvantaged  backgrounds, 
immigrant  students  and  students  from  diverse  ethnic  minorities  in  order  to  reduce  the 
impact of socio-economic background on education outcomes. 

Another  factor  that  influences  school  performance  is  how  long  immigrant  students  have 
resided in the host country. Studies indicate that native students outperform immigrants, 
including  those  that  arrived  in  the  host  country  during  their  childhood  (Dustmann  & 
Theodoropoulos, 2010; Murat, 2011; OECD & European Union, 2015). In addition, there 
is also evidence of the high relevance of host country language knowledge, together with 
family  background,  as  a  driver  of  the  immigrant-native  gaps  in  many  countries 
(Dustmann  et  al.,  2012;  OECD,  2016c).  Specifically,  language  knowledge  can  help  to 
reduce the immigrant-native educational gap.  

In what regards the influence of early tracking, Hanushek & Wößmann (2006) show that, 
in general, early tracking5 of students in secondary schools based on their academic skills 
increases  educational  inequality.  This  in  in  line  with  a  recent  study  from  Jakubowski  & 
Pokropek  (2015)  that  finds  that  while  an  early  tracking  policy  might  not  be  harmful  for 
the  best  students,  it  can  lower  the  performance  development  of  the  weakest  students, 
such as immigrants. There is also evidence that tracking at later stages does not always 
reduce score gaps between immigrant and native students, although it can contribute to 
improve  educational  opportunities  of  students  lacking  proficiency  in  the  language  of 
instruction  (Ruhose  &  Schwerdt,  2016).  In  addition,  in  some  EU  Member  States  early 

4 Hippe et al. (2016) has been used for the following review of the literature on immigrant students. 
5 Tracking in this context is the differentiation of school curricula into vocational and academic tracks. 

3 

tracking  systems  are  viewed  as  an  obstacle  for  the  integration  of  immigrants  as,  in 
general,  the  selection  into  tracks  occurs  before  children  become  skilled  in  the  host 
country  language  (De  Paola  &  Brunello,  2016;  Lüdemann  &  Schwerdt,  2010). 
Consequently, Ruhose & Schwerdt (2016) suggest that action must be taken to improve 
the educational opportunities of children from less integrated families calling for a more 
comprehensive school system that contributes to the integration of immigrant students. 

The recent literature has also pointed out the importance of class and school composition 
in  fostering  immigrants’  integration.  Having  a  high  share  of  immigrants  in  the  class  or 
school  has  a  negative  effect  on  immigrants’  performance6  (De  Paola  &  Brunello,  2016). 
These  results  indicate  that introducing  a limit in  the  share  of  immigrant  students  in  the 
classroom  is  a  supportive  policy  that  might  reduce  immigrants’  educational  gap  (similar 
policies have been introduced in Denmark and Belgium; see De Paola & Brunello, 2016). 
Additionally, retaining and attracting more advantaged students in schools that also host 
immigrant  students  could  be  a  supportive  policy  to  improve  immigrants’  educational 
opportunities  (OECD,  2015b).  In  contrast,  PISA  2015  results  do  not  show  a  negative 
influence  of  immigrant  concentration  in  schools  on  science  scores,  after  controlling  for 
the school’s socio-economic composition (OECD, 2016c).  

The  literature  also  reveals  that  free  pre-school  programmes  for  immigrants  can  help  to 
increase equity for students with an immigrant background (De Paola & Brunello, 2016). 
Several  recent  system-level  reforms  have  also  been  adopted  to  design  more  inclusive 
education  systems,  through  structural  changes  to  education  systems  or  more  targeted 
approaches, such as reducing grade repetition or raising the age of early tracking.7 

In  sum,  the  evidence  shows  that  there  are  many  factors  influencing  immigrants’ 
integration  and  success  at  school  and  that,  while  some  may  be  common  across  MS, 
others are country specific.  

2.2

  Early school leaving and educational expectations 

Early school leaving is an important and policy-relevant issue, as it produces high costs 
for  the  individual  and  society  (Brunello  &  De  Paola,  2013).  ESL  miss  out  on  additional 
years  at  school,  which  is  costly  because  “an  additional  year  of  schooling  can  increase 
individual lifetime earnings by between 4% and 10%” (European Commission, 2013b, p. 
11).  Thus,  individuals  cannot  reap  the  benefits  of  further  educational  studies  although 
these advantages are manifold, not only in economic terms, but also in other areas. For 
example,  on  average,  individuals  who  stay  longer  in  education  have  higher  job 
satisfaction, take better informed decisions for  health, social life, etc. and increase their 
non-cognitive skills. On the other hand, the state also faces higher costs for its finances 
(e.g., potentially higher unemployment benefit payments) and its social welfare, as early 
school  leaving  has  detrimental  impacts  on  crime  rates,  and  on  attitudes  towards 
immigrants and other minority groups, etc. (Brunello & De Paola, 2013). 

The European Commission et al. (2014) names a number of factors that are most closely 
related  to  early  school  leaving,  such  as  low  socio-economic  background,  being  an 
immigrant  student  and  being  male.  In  particular,  early  school  leaving  is  particularly 
widespread  among  immigrant  groups.8  However,  the  most  important  determinant  of 

6  Schneeweis  (2015)  shows  that,  in  terms  of  grade  repetition  and  track  attendance  of  students  with  an 
immigrant  background,  there  is  a  negative  association  with  high  share  of  immigrants,  particularly  for 
students of the same ethnic group. 

7  Table  5  (in  appendix)  shows  that  some  MS,  like  DE,  FI,  IE  and  SI,  have  recently  implemented  educational 
reforms,  strategies  or  policies  aimed  at  providing  equal  opportunities  in  education  for  disadvantaged 
students. 

8  According  to  the  European  Commission  et  al.  (2014,  p.  39),  “[t]he  proportion  of  foreign-born  compared  to 
those  born  in  the  reporting  country  is  nearly  three  to  five  times  as  high  in  Greece,  Slovenia,  Croatia, 
Austria and Switzerland. In Belgium, Spain, France, Italy, Cyprus, Finland and Sweden, the rates of early 
leavers  among  foreign-born  students  are  still  around  twice  as  high.  […]  In  Denmark,  Ireland,  Malta,  the 
Netherlands  and  Portugal,  the  differences  between  the  two  groups  are  relatively  lower,  even  though  in 

4 

early  school  leaving  is  not  the  immigrant  status  per  se,  but  the  often  associated  low 
socio-economic background.  

However,  in  many  international  student  assessments  like  PISA  it  is  most  often  not 
possible  to  measure  the  accomplished  educational  level  of  students  but  only  their 
‘expected’  level,  as  the  students  are  not  followed  through  their  subsequent  educational 
career  once  they  have  completed  the  assessment.  That  is,  one  cannot  check  whether 
‘expected’  educational  levels  are  identical  with  future  ‘realised’  educational  levels.  In 
principle there may be differences between the expected and the actually realised level, 
which may be influenced by personal ability, socio-economic status (e.g., due to financial 
issues)  and  cultural  values  (which  may  be  different  between  natives  and  (among 
different) migrant groups). Nevertheless, the literature emphasises that overall expected 
education  levels  correlate  very  highly  with  those  actually  realised.  The  analysis  of 
expectations  was  first  introduced  about  50  years  ago  by  Sewell  &  Shah  (1968),  Sewell, 
Haller,  &  Ohlendorf  (1970)  and  Sewell  &  Hauser  (1972).  These  authors  found  in  their 
models that parents’ and students’ educational expectations were predicting educational 
achievement  quite  well  (see  Gutierrez  &  Lopez-Agudo,  2016;  Minello,  2014).  This 
association  has  become  quite  established  in  the  literature.  In  fact,  authors  such  as 
Portes, Aparicio, Haller, & Vickstrom (2010, p. 793) state that the “relationship between 
expectations  and  achievement  is  arguably  one  of  the  best  established  facts  in  social 
science. The rationale is obvious: if a young person aims at some lofty goal, she may not 
achieve  it;  but  if  she  does  not  aim  high  in  the  first  place,  she  will  surely  not  get  there. 
Stated  in  this  form,  ambition  becomes  a  prerequisite  –  a  necessary  condition  –  for 
achievement”.  The  OECD  confirms  these  findings,  indicating  that  “[e]xpectations  for 
higher  education  and  careers  are  often  self-fulfilling  prophecies:  students  who  hold 
ambitious  –  but  realistic  –  expectations  for  their  future  are  more  likely  to  put  greater 
effort into their learning and make better use of the education opportunities available to 
them” (OECD, 2015a, p. 18). 

At  the  same  time,  immigrant  students  may  have  differing  educational  goals  and 
expectations  than  their  native  counterparts.  Their parents’  migration  decision  was  often 
motivated by the search for better work and education opportunities for themselves and 
their  children  (Goldenberg,  Gallimore,  Reese,  &  Garnier,  2001;  Kao  &  Tienda,  1995; 
Phalet,  Andriessen,  &  Lens,  2004).  While  work  opportunities  may  sometimes  be  more 
important  than  educational  prospects  (Hagelskamp,  Suárez-Orozco,  &  Hughes,  2010; 
Massey  &  Taylor,  2004),  a  very  good  education  for  their  children  is  often  a  key 
motivating  factors  for  immigrant  parents  (Dustmann  &  Glitz,  2011;  OECD,  2017b).  In 
consequence,  the  different  backgrounds  and  the  different  reasons  behind  the  migration 
decision  may  potentially  induce  immigrant  students  to  have  specific  educational 
expectations.  This  is  one  of  the  reasons  why  this  issue  has  recently  received  more 
attention  by  researchers  (Feliciano,  2006;  Minello,  2014;  Minello  &  Barban,  2012).  For 
example,  educational  expectations  of  various  immigrant  groups  tend  to  be  high  in  a 
number  of  MS  (Brinbaum  &  Cebolla-Boado,  2007;  Jonsson  &  Rudolphi,  2010;  Kristen  & 
Dollmann,  2010;  Relikowski,  Yilmaz,  &  Blossfeld,  2012;  Salikutluk,  2016;  Teney, 
Devleeshouwer, & Hanquinet, 2013).  

This finding can theoretically be  explained in four different ways (see  Salikutluk, 2016). 
The  first  is  the  immigrant  optimism  approach.  Even  if  the  parents  were  not  successful 
and  find  themselves  in  the  lower  strata  in  the  destination  country,  their  children  may 
continue  the  pursuit  of  their  parents’  goals,  for  which  education  is  a  key  factor  (Heath, 
Rothon, & Kilpi, 2008; Tjaden & Hunkler, 2017). Second, immigrants may positively react 
to  difficulties  in  the  destination  country  by  trying  to  overcome  these  hurdles,  as 
summarised  in  the  blocked  opportunities  assumption  (e.g.,  Heath  &  Brinbaum,  2007). 
Third, it is possible that immigrant parents do  not have sufficient information about the 
educational system of the country they have migrated to, so that they underestimate the 

some of these countries, high rates of migrants exist. Finally, one country stands out as an exception: the 
United  Kingdom  has  a  slightly  higher  rate  of  students  leaving  education  and  training  early  among  those 
born in the country”. 

5 

 
                                                                                                                                    
requirements  and  standards  in  order  to  attain  high  educational  attainment  (Kao  & 
Tienda, 1995). Finally, the so-called ‘influence of significant others’ hypothesis states that 
close  relationships  within  and  outside  the  family  (parents,  siblings,  peers,  friends,  etc.) 
may have  various positive and negative influences on the  expectations of students, and 
immigrant  students  in  particular  (e.g.,  Gabay-Egozi,  Shavit,  &  Yaish,  2014;  Nauck  & 
Kohlmann, 1999). 

However,  expectations  of  immigrant  students  are  importantly  influenced  also  by  their 
origin  countries  and  the  level  of  resources  that  is  available  to  the  origin  population  and 
the  socio-economic  status  of  immigrants  in  their  home  (Blasko,  Pokropek,  &  Sikora, 
2017).  Salikutluk  (2016)  shows  that  students  with  Turkish  origins  have  higher 
educational  expectations  than  the  native  population  in  Germany,  while  this  is  not  the 
case  for  students  coming  from  countries  of  the  former  Soviet  Union.  Upward  mobility 
seems to be driving the high educational ambitions of the Turkish students. Therefore, it 
is  relevant  to  distinguish  immigrant  populations  according  to  their  country  of  origin. 
Differences  between  EU  and  non-EU  immigrants  may  be  especially  interesting  in  this 
context.  

In addition, many other factors may be related to educational expectations. For example, 
the  OECD  notes  that  “[s]tudents’  expectations  of  further  education  are  influenced  by 
education  policy,  particularly  the  degree  of  sorting  students  into  different  education 
tracks”  (OECD,  2017b,  p.  104).  Thus,  in  our  following  analyses  we  will  also  assess 
various ways by which education policy is related to educational expectations.  

3  Data and methodology 

3.1

  Variables included in this study 

For the purpose of this report, we included most of the variables the OECD reports when 
presenting and discussing PISA 2012 and 2015 cross-country differences and similarities 
in  the  performance  and  characteristics  of  students  with  immigrant  background  and 
students’  expectations  of  further  education  (OECD,  2015a,  2015c,  2016c,  2017b).  In 
addition,  we  include  two  variables  (epistemological  beliefs,  truancy)  which  have  been 
shown  to  be  major  predictors  of  PISA  performance  in  the  newest  PISA  2015  round 
(Hippe, Jakubowski, & Araújo, 2017; OECD, 2016c) and which are also susceptible to be 
relevant drivers of expected dropout rates.  

Expected  early  school  leaving  is  measured  via  the  student  responses  to  the  question 
“Which  of  the  following  do  you  expect  to  complete?”,  which  allows  students  to  choose 
among different ISCED levels. For our purposes, we code expected early school leaving in 
a  binary  way,  this  means  that  leaving  school  early  is  either  expected  (when  students 
choose the category ISCED 2) or not expected (in the cases that students choose a level 
higher  than  ISCED  2).  The  choice  of  this  cut-off  point  is  according  to  the  official  EU 
definition  of  early  school  leavers  who  have  ISCED2  as  their  highest  school  attainment 
level. 

In addition, the explanatory variables used in this brief can be classified into two groups 
of  predictors:  variables  that  refer  to  students  and  variables  that  relate  to  schools  (see 
Figure 2).9  

9 See appendix for more details on the included variables. 

6 

 
 
 
 
 
                                           
Figure 2. Explanatory variables used in the analysis 

Truancy 

Epistemological beliefs 

Socio-economic status (ESCS) 

Immigrant background (1st & 2nd generation) 

Student level 

Language spoken at home 

Sense of belonging 

Age at arrival 

Grade repetition 

Gender 

Science score 

Concentration of immigrants (school mean) 

Programme designation 

Programme orientation 

School level 

ISCED level 

Socio-economic status (ESCS) (school mean) 

Expected ESL level (school mean) 

Science score (school mean) 

In consequence, at the student level we include the following variables: 

Truancy  measures  whether  students  are  skipping  school  days,  skipping  classes,  being 
late  at  school.  In  fact,  students  were  asked  several  questions  about  truancy.  We  used 
their  responses  to  estimate  an  index  of  truancy  using  the  principal  component  analysis 
based on polychoric correlations for ordinal responses (Kolenikov & Ángeles, 2004). The 
intuition is that skipping classes may have a negative association with performance and 
thus a positive one with the expectation of dropping out early.  

Epistemological  beliefs  reflect  how  students  see  science  and  scientific  enquiry.  Students 
who  score  higher  in  the  PISA  index  of  epistemological  beliefs  argue  that  “scientific 
knowledge is tentative" (to the extent that students recognise that scientific theories are 
not  absolute  truths,  but  evolve  over  time)  and adhere  “to  beliefs  about  the  validity  and 
limitations  of  empirical  methods  of  enquiry  as  a  source  of  knowing”  (OECD,  2016c,  pp. 
99–100).  The  index  is  standardised  to  have  a  mean  of  0  and  a  standard  deviation  of  1 
across OECD countries (weighting each country equally). 

Economic, Social and Cultural Status (ESCS) is an OECD index measuring student socio-
economic background. PISA measures ESCS with an extensive set of questions related to 
parent  occupation,  education  and  household  cultural,  educational  and  economic 
resources. It is usually positively associated with PISA scores. The index is standardised 
to  have  mean  of  0  and  standard  deviation  of  1  across  OECD  countries  (weighting  each 
country  equally).  A  lower  socio-economic  background  of  students  generally  increases 

7 

 
early  dropout  rates,  as  has  been  shown  by  a  multitude  of  studies  on  this  subject 
(European Commission et al., 2014). 

Immigrant  background  refers  to  first  or  second  generation  immigrant  students.  As  first 
and second generation students have different immigration backgrounds, the results may 
in many cases vary according to each group. In consequence, we include this distinction 
among immigrant students. 

Language  spoken  at  home  controls  for  the  fact  that  the  language  taught  at  school  and 
the  language  spoken  at  home  may  be  different.  In  consequence,  a  student  may  have 
more  difficulty  in  the  subjects  at  school  and  in  socialising  with  schoolmates  when  the 
same  language  is  not  practiced  in  both  home  and  school  environments.  Thus,  not 
speaking  the  language  at  home  has  also  been  shown  to  be  negatively  associated  with 
PISA  scores.  In  MS  such  as  Czech  republic,  Finland,  Slovenia  and  Sweden,  the  share  of 
first  generation  students  who  do  not  speak  the  same  language  at  home  as  in  school  is 
more than 80 %, while in Croatia this is true only for less than 10 % (OECD, 2015a).  

Sense  of  belonging  shows  how  psychologically  well  students  feel  integrated  and 
belonging  to  the  school  they  attend.  This  is  an  important  measure  of  the  social 
integration  of  immigrant  students.  The  OECD  concludes  that  the  sense  of  belonging 
varies  widely  among  EU  MS.  For  example,  in  the  UK  newly  arriving  (first  generation) 
immigrant  students  have  a  higher  feeling  of  belonging  than  natives  and  second 
generation  students.  Both  of  the  latter  two  groups  have  a  similar  level  of  belonging. 
Second, e.g. in France second generation students have the lowest sense of belonging of 
all  groups.  In  contrast,  integration  is  more  progressive  in  Italy,  Spain  and  Sweden.  In 
these  MS,  second  generation  students  have  similar  (high)  values  to  natives,  while  first 
generation students have lower values (OECD, 2015a). 

Age  at  arrival  provides  information  on  when  an  immigrant  student  has  arrived  in  the 
destination  country  in  which  she  took  the  PISA  test.  The  intuition  for  including  this 
variable  is  that  it  is  easier  for  a  student  to  learn  and  integrate  into  a  new  cultural  and 
linguistic  environment  the  younger  she  is  (OECD,  2016c).  More  specifically,  the  OECD 
calls  this  also  the  ‘late-arrival  penalty’:  “[i]n  most  OECD  countries,  immigrant  students 
who arrived at the age of 12 or older – and have spent at most four years in their new 
country  –  lag  farther  behind  students  in  the  same  grade  in  reading  proficiency  than 
immigrants who arrived at younger ages” (OECD, 2015a, p. 10). 

Grade  repetition  is  a  policy  that  is  common  in  some  European  countries  and  has  been 
shown  to  influence  school  leaving  negatively  (European  Commission  et  al.,  2014).  In 
addition,  previous  research  with  PISA  data  suggests  that  repeating  a  grade  is  not 
associated  with  improved  student  performance  and  shows  a  negative  association  with 
student  attitudes  (Ikeda  &  García,  2014).  At  the  same  time,  this  policy  is  very  costly 
(Benhenda & Grenet, 2015). In PISA 2015, and in line with findings from previous PISA 
rounds, students who have repeated a grade at least once score lower (OECD, 2016c).  

Finally, gender is a dummy variable, taking one for female students. Research has shown 
that  girls  may  have  different  educational  expectations  than  boys  (e.g.,  Sikora  &  Biddle, 
2015).  For  example,  girls’  educational  expectations  are  much  higher  in  Italy  (Minello, 
2014).  In  general,  girls  also  have  lower  realised  early  dropout  rates  than  boys  at  the 
European level (European Commission, 2016; European Commission et al., 2014). 

In  some  specifications,  we  also  include  students’  science  scores.10  In  fact,  science 
achievement  is  the  main  domain  in  PISA  2015.  This  means  that  science  was  measured 
with the highest possible precision and every student taking the 2015 PISA test answered 
a  number  of  science-related  test  items.  While  reading  and  mathematics  were  also 

10 Student achievement is reflected by a set of the so-called plausible values that reflect student outcomes and 
allow  for  estimating  measurement  error.  In  PISA 2015,  ten  plausible  values  are  provided  in  the  datasets 
and  the  analysis  should  replicate  every  estimation  ten  times  with  each  plausible  value.  The  results 
averaged across ten replications provide unbiased estimates of student achievement. In the calculation of 
standard  errors  variation  across  ten  replications  is  included  using  special  formulas  that  add  estimates  of 
measurement error to the estimates of sampling error. 

8 

 
                                           
measured  in  2015,  the  number  of  test  items  for  these  domains  was  smaller  and  the 
resulting measurement is less precise. Thus, we decided to include science performance 
and not reading or mathematics performance. In PISA, science is defined as “the ability 
to  engage  with  science-related  issues,  and  with  the  ideas  of  science,  as  a  reflective 
citizen.  A  scientifically  literate  person  is  willing  to  engage  in  reasoned  discourse  about 
science  and  technology,  which  requires  the  competencies  to  explain  phenomena 
scientifically,  evaluate  and  design  scientific  enquiry,  and  interpret  data  and  evidence 
scientifically” (OECD, 2016b, p. 13). We use this variable to proxy for unobserved ability. 

In addition, we consider the following variables at the school level: 

Concentration  of  immigrant  students  in  schools  indicates  the  relative  prevalence  of 
immigrant  students  in  schools  (averaged  for  schools).  While  there  can  be  some 
advantages  of  arriving  in  a  school  with  students  from  a  similar  cultural  background,  in 
many  cases  the  concentration  of  immigrants  is  related  to  a  concentration  of  socio-
economically disadvantaged students (OECD, 2015a). In PISA 2015, a high concentration 
of  immigrant  students  in  schools  is  not  associated  with  poorer  student  performance, 
while,  individually,  immigrant  students  on  average  show  lower  performance  in  EU  MS 
(OECD, 2016c). 

We also include programme designation, which provides further information on the study 
programme  (general  or  vocational  level  giving  access  to  next  level,  giving  direct  access 
to  labour  market  and  modular).  Similarly,  programme  orientation  indicates  whether 
students  are  enrolled  in  general,  pre-vocational  or  vocational programmes.  It  should  be 
noted  that  in  many  MS  included  in  this  analysis  all  15-year-olds  are  in  academic  or 
general  schools.  Thus,  the  results  should  be  interpreted  cautiously.  However,  this 
variable provides interesting insights for some MS with a sufficient number of students in 
vocational  education.  Early  tracking,  before  the  age  of  15,  has  been  shown  to  increase 
educational  inequality  (Ruhose  &  Schwerdt,  2016).  On  the  other  hand,  at  the  country 
level,  MS  “with  a  relatively  weak  VET  system  tend  to  have  a  higher  problem  of  early 
leaving,  likely  due  to  the  lack  of  sufficiently  attractive  non-academic  programmes” 
(European Commission et al., 2014, p. 13). 

We also control for the ISCED level. In fact, PISA uses as criteria the age of students for 
inclusion  in  the  survey,  i.e.  only  15  years  olds  are  allowed  to  participate.  As  a 
consequence, some students are already in an ISCED3 year, and many others are still in 
ISCED2. While this does not mean that they  may expect to complete  ISCED3, this may 
potentially  influence  the  results.  Therefore,  we  control  for  this  factor  in  the  regression 
analysis.11 

Finally,  we  also  include  school  means  for  the  ESCS,  expected  early  school  leaving  and 
science  scores.  In  other  words,  the  student’s  data  were  aggregated  and  averaged  for 
schools to be able to control for further school environment effects. 

To  get  a  clear  picture,  we  provide  below  a  summary  table  of  the  variables  and  their 
descriptions  that  are  used  in  this  brief  (see  Table  1).  To  simplify  the  reading  of  the 
regression  tables  later  on,  we  also  include  information  on  the  reference  category  in  the 
multilevel regressions. 

Table 1. Description of student-level variables 

Variable  
Expected early school leaving 

Gender 
Student's ESCS 
1st generation 
2nd generation 
Language at home 

Description 
Leaving school early (after ISCED 2) is expected or not. Reference category: not 
expecting to leave school early .  
Male or female. Reference category: male 
Measures student socio-economic background 
1st generation immigrant or native student. Reference category: native 
2nd generation immigrant  or native student. Reference category: native 
Language taught at school and the language spoken at home are different. Reference 

11 See also details on the minimum age during compulsory schooling in the appendix. 

9 

 
                                           
Age at arrival 
Sense of belonging 
Epistemological beliefs 
Truancy 

Grade repetition 
Science scores 
(Programme) Designation 

(Programme) Orientation  

ISCED Level 

category: languages are not different 
Measures when an immigrant student has arrived in the destination country  
Measures how well students feel integrated and belonging to the school they attend 
Measures how students see science and scientific enquiry 
Measures whether students are skipping school days, skipping classes, being late at 
school (index) 
Student has repeated a grade or not. Reference category: not having repeated a grade. 
Measures science achievement 
Student attends: general programmes designed to give access to the next programme 
level, or programmes designed to give access to vocational studies at the next 
programme level, or giving direct access to labour market, or modular. Reference 
category: general programmes 
Student attends: general, pre-vocational or vocational programmes (and in Luxembourg, 
also modular programmes). Reference category: general 
Student attends: ISCED2 or ISCED3. Reference category: ISCED2 

3.2

  Sample sizes 

Given  the  varying  distribution  of  immigrants  in  the  MS,  we  can  expect  that  immigrant 
student populations have quite different sample sizes and characteristics in the MS in the 
PISA  dataset.  In  addition,  the  large  variance  found  in  the  data  indicates  that  it  may  be 
worth considering the difference between EU and non-EU immigrants. However, the low 
shares  of  immigrants  in  general,  and  EU  migrants  in  particular,  may  lead  to  very  low 
sample sizes, which may not allow an in-depth analysis for some MS. 

To  define  minimum  threshold  sizes  for  the  inclusion  of  countries  in  our  regressions,  we 
follow the recommendations set up by the OECD (see e.g., OECD, 2011, p. 179), which is 
at least 30 students from at least 5 schools. The details on the sample sizes in our study 
can be seen in Table 2. More specifically, immigrants can be considered for all MS except 
PL and RO, while both first and second generation immigrants can be included for all MS 
except BG, PL and RO.  

In  this  brief,  our  aim  is  also  to  characterise  the immigrant  populations  according  to  the 
distinction EU-/non-EU origin country. Given the low sample sizes in most MS, this more 
detailed  differentiation  can  only  be  done  for  AT,  BE,  CY,  CZ,  DE,  FI,  IE,  IT,  LU,  MT,  PT 
and the UK (the differenciation between first or second generation would only be possible 
for  fewer  MS)  in  the  regression  models.  In  contrast,  non-EU  immigrant  populations  are 
generally  more  numerous,  so  that  non-EU  immigrants  can  be  analysed  in  all  MS  apart 
from CY, PL and RO. 

Table 2. Sample size by immigrant status 

MS 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

Total  

Immigrants 

EU migrants 

Non-EU immigrants 

Total  Natives  Migrants 

1st gen.   2nd gen.  

Total 

1st gen.   2nd gen.  

Total 

1st gen.   2nd gen.  

6928 

9363 

5707 

4942 

6788 

5691 

6967 

5459 

5609 

7812 

5650 

4712 

6562 

4724 

5281 

4899 

1319 

1551 

57 

230 

226 

967 

1686 

560 

477 

773 

28 

167 

109 

215 

368 

37 

842 

778 

29 

63 

117 

752 

1318 

523 

237 

467 

 0 

230 

48 

135 

2 

0 

141 

355 

 0 

167 

24 

42 

1 

0 

96 

112 

 0 

63 

24 

93 

1 

0 

1082 

1084 

57 

0 

178 

832 

1684 

560 

336 

418 

28 

0 

85 

173 

367 

37 

746 

666 

29 

0 

93 

659 

1317 

523 

10 

 
 
 
 
 
EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

5420 

6577 

5794 

5946 

5621 

5566 

5495 

4893 

5896 

5564 

5184 

5016 

5419 

4735 

11232 

10333 

6260 

5170 

4797 

3489 

5209 

4428 

7179 

4818 

5283 

6297 

6165 

6039 

2498 

4554 

3314 

4649 

4417 

6757 

4799 

4385 

5779 

6095 

527 

681 

230 

762 

605 

147 

760 

899 

221 

170 

556 

126 

252 

100 

57 

581 

526 

36 

357 

125 

104 

510 

505 

90 

179 

373 

185 

 0 

 0 

38 

 0 

 0 

 0 

157 

226 

 0 

 0 

 0 

33 

 0 

 0 

 0 

95 

161 

0 

 0 

 0 

5 

 0 

 0 

 0 

62 

65 

 0 

2672 

1093 

1579 

1918 

780 

1138 

243 

175 

560 

11 

422 

19 

898 

518 

70 

43 

123 

114 

7 

236 

7 

395 

223 

35 

200 

52 

446 

4 

186 

12 

503 

295 

35 

 0 

70 

22 

 0 

60 

 0 

 0 

13 

25 

0 

61 

18 

 0 

34 

 0 

 0 

8 

16 

 0 

9 

4 

 0 

26 

 0 

 0 

5 

9 

527 

681 

192 

762 

605 

147 

603 

673 

221 

754 

243 

105 

538 

11 

362 

19 

898 

505 

45 

170 

556 

93 

252 

100 

57 

486 

365 

36 

313 

43 

62 

96 

7 

202 

7 

395 

215 

19 

357 

125 

99 

510 

505 

90 

117 

308 

185 

441 

200 

43 

442 

4 

160 

12 

503 

290 

26 

UK 

11679 
13391 
EU28  175982  157254 

1712 
18728 

1086 
7940 

626 
10788 

482 
4130 

441 
2377 

41 
1753 

1230 
14598 

645 
5563 

585 
9035 

Note: gen. = generation; orange = insufficient number of observations. 

3.3

  Econometric model 

Multilevel  regression  models  are  well-suited  to  the  analysis  of  large  scale  international 
student assessment surveys because they recognize the hierarchical structure of the data 
with students nested in schools. Accordingly, in our analysis the two-level model reflects 
that students are nested in schools. The associations between performance and student-
level  or  school-level  factors  can  also  be  decomposed  into  within-school  and  between-
school associations. Thus, with these models it is possible to analyse how differences in 
key policy-relevant variables are associated with student and school characteristics.12 

The  basic  two-level  model  with  random  effects  can  be  described  by  two  equations.  The 
first equation describes the model for the  student-level where i is an index for  students 
and j is an index for schools: 

(cid:1)(cid:2)(cid:3) (cid:4) (cid:5)(cid:6)(cid:3) (cid:7) (cid:5)(cid:8)(cid:9)(cid:2)(cid:3) (cid:7) (cid:10)(cid:2)(cid:3). 

In  this  equation,  (cid:1)(cid:2)(cid:3)  is  the  outcome  variable,  in  this  case  expected  early  school  leaving. 
This  implies  that  we  are  using  a  multilevel  binary  logistic  model.  In  other  words,  the 
outcome variable is binary, that is it can only take the value 0 (i.e., early school leaving 
is not expected) or 1 (i.e., early school leaving is expected). Thus, we are analysing the 
factors  that  affect  the  probability  of  expecting  to  leave  school  early  in  these  models.  In 
addition, (cid:9)(cid:2)(cid:3) is a vector of student-level characteristics and (cid:10)(cid:2)(cid:3) is a student-level error.  

The second level equation describes school level intercepts: 

(cid:5)(cid:6)(cid:3) (cid:4) (cid:12)(cid:6)(cid:6) (cid:7) (cid:12)(cid:6)(cid:8)(cid:13)(cid:3) (cid:7) (cid:14)(cid:6)(cid:3), 

12 For more information on missing data imputation and weights, see appendix. 

11 

 
 
                                           
where  (cid:13)(cid:3)  is  a  school-level  predictor  (or  a  set  of  predictors)  and  (cid:14)(cid:6)(cid:3)  is  a  random  error 
component at the school-level. 

In  our  multilevel  logit  regression  analysis  (see  section  4.2),  we  first  consider  the  entire 
EU,  controlling  for  country  fixed  effects  and  including  a  number  of  variables  that  are 
specifically  addressing  (the  characteristics  of)  immigrant  students.  In  this  model,  we 
weight all countries by their population size and we adjust school-level weights to obtain 
proper estimates of between-school variance (see Rabe-Hesketh & Skrondal, 2006). In a 
second  step,  we  analyse  each  MS  individually.  Finally,  we  also  distinguish  between  EU 
and non-EU immigrants. 

4  Empirical analysis 

4.1

  Descriptives  

4.1.1  Characteristics of students with immigration background  

Before  analysing  immigrant  students  in  PISA  2015  data  in  more  detail,  we  begin  by 
showing  the  distribution  of  the  actual  total  immigrant  population  in  the  European 
countries. Eurostat (2017c) data show for 2016 that the share of immigrants (defined as 
individuals  not  residing  in  their  country  of  birth)  within  the  total  population  varies 
substantially  among  MS.  The  highest  rates  of  immigrants  are  recorded  in  LU  (45  %), 
while the lowest are to be found in many new MS in Eastern Europe.  

Figure 3. Share of immigrants in total population in European countries, 2016  

0.17 - 0.45
0.15 - 0.17
0.13 - 0.15
0.12 - 0.13
0.08 - 0.12
0.04 - 0.08
0.02 - 0.04
No data

Note:  7  classes  automatically  defined.  An  immigrant  is  defined  as  a  person  not  born  in  the  country  he  is  a 

resident in. Only those countries are shown in the map for which Eurostat provides data. 

Source: calculated from data by Eurostat (2017c). Background map: © EuroGeographics for the administrative 

boundaries. 

An  interesting  aspect  is  to  differentiate  between  immigrants  born  in  another  MS  and 
immigrants born outside the EU. EU migrants make up a high share of all immigrants in 
SK (83 %), LU (75 %) and IE (69 %), while particularly in the Baltic MS, HR and PT most 
immigrants  come  from  countries  outside  of  the  EU  (EE  having  the  lowest  share  of  EU 
migrants  with  only  10  %).  The  low  shares  could  be  explained  by  the  specific  historical 
and cultural relationships of these countries to various non-EU countries (the Baltics with 

12 

 
 
 
 
Russia, HR with other former parts of Yugoslavia, PT with its former colonies, in particular 
Brazil).  

Figure 4. Share of EU migrants among all immigrants in European countries, 2016  

0.64 - 0.83
0.45 - 0.64
0.40 - 0.45
0.35 - 0.40
0.29 - 0.35
0.27 - 0.29
0.10 - 0.27
No data

Note:  7  classes  automatically  defined.  An  immigrant  is  defined  as  a  person  not  born  in  the  country  he  is  a 

resident in. Only those countries are shown in the map for which Eurostat provides data. 

Source: calculated from data by Eurostat (2017c). Background map: © EuroGeographics for the administrative 

boundaries. 

After  this  first  impression  on  the  total  immigrant  population  in  Europe,  we  now  turn  to 
the  immigrant  students’  data  in  PISA  2015.  Across  all  EU  MS,  11  %  of  students  in  the 
PISA  2015  sample  have  an  immigrant  background  (see  Figure  5).  The  share  is  equally 
divided among first (5 %) and second (6 %) generation students. Most of the immigrants 
come from outside the EU, with a higher incidence of second generation immigrants. LU 
has  by  far  the  highest  share,  with  more  than  50  %  of  students  having  an  immigrant 
background,  followed  by  DK  and  AT,  where  almost  a  quarter  and  a  fifth  of  students 
respectively are immigrants. Very low shares of immigrants are included in many new MS 
(e.g.,  BG,  CY,  CZ,  HU  and  SK,  in  addition  to  PL  and  RO  where  the  immigrant  numbers 
are even too low to be presented here). 

13 

 
 
 
Figure 5. Share of immigrant students in PISA 2015 

n
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100

80

60

40

20

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T
A

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B

G
B

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C

E
D

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D

E
E

L
E

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S F
E

R
F

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E

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T

I

T
L

U
L

V
L

T
M

L
N

T
P

I

E S
S

K
S

K
U

Native
2nd gen. - non EU
1st gen. - non EU

2nd gen. - EU
1st gen. - EU

Note:  Shares  are  calculated  with  regard  to  the  total  number  of  students.  Shares  not  presented  for  MS  with 

insufficient number of observations. 

Out of all immigrants, 42 % are first generation students (see Figure 6). The MS with a 
rather  massive  recent  immigration  are  e.g.,  CY,  ES,  IE,  MT  and  UK,  while  new  arrivals 
are a small minority among immigrants in EE, HR, LT and LV.  

14 

 
 
 
 
 
Figure 6. Share of first generation among immigrant students in PISA 2015 

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

T
A

E
B

Y
C

Z
C

E
D

K
D

E
E

L
E

S
E

I
F

R
F

R
H

E
U I
H

T
I

T
L

U
L

V
L

T
M

L
N

T
P

I

E S
S

K
U

8
2
U
E

Note: Shares are calculated with regard to the total number of students with immigrant background. Shares not 

presented for MS with insufficient number of observations. 

Among  the  immigrant  student  population,  only  about  one  out  of  five  is  an  EU  migrant, 
while  most  immigrant  students  come  from  countries  outside  the  EU  (see  Figure  7). 
However,  EU  migrants  are  unequally  distributed  among  MS.  In  particular,  the  highest 
share of EU migrant students is in CY, where all immigrant students come from other MS. 
In addition, LU has a very high share with more than 70 % of migrant students coming 
from  another  MS.  We  can  see  from  these  figures  that  LU  has  a  very  specific  migrant 
population, which is very high in comparison to other MS, but also more European (more 
exactly,  from  other  MS)  than  it  is  the  case  for  other  MS.  High  shares  of  EU  migrants 
among all immigrants are also present in MT (40 %), SK, BE and UK (around 30 %).  

15 

 
 
 
 
 
 
Figure 7. Share of EU students among immigrant students in PISA 2015 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

T
A

E
B

Y
C

Z
C

E
D

K
D

E
E

L
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S
E

I
F

R
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U I
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T
I

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U
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V
L

T
M

L
N

T
P

I

E S
S

K
U

8
2
U
E

Note: Shares are calculated with regard to the total number of students with immigrant background. Shares not 

presented for MS with insufficient number of observations. 

The  number  of  immigrants  has  also  evolved  over  time.  Compared  to  2006,  there  has 
been  an  increase  in  immigrant  numbers  in  most  MS,  while  in  some  new  MS  immigrant 
numbers have decreased in the last decade (see Figure 8). The outstanding numbers for 
LU  are  to  an  important  extent  due  to  immigrants  entering  the  school  system  rather 
recently  (first  generation).  This  fact  shows  the  attraction  that  LU  exerts  among  other 
countries,  and  as  we  know  from  the  figures  above,  particularly  among  other  MS.  The 
reverse  case we  can see in IE und UK,  which have the second and third highest overall 
change  in  student  numbers,  respectively.  In  these  MS,  it  is  the  second  generation  of 
migrants  that  prevails  in  number.  In  addition,  in  AT,  the  subsequent  MS  in  fourth 
position, the percentage changes are driven by recent arrivals from the first generation, 
while the changes in the second generation are not even statistically significant.  

16 

 
 
 
 
 
Figure 8. Percentage change in number of immigrants between 2006 and 2015 

LU

IE

UK

AT

SE

BE

IT

ES

EL

DK

DE

FI

CZ

HU

BG

SK

RO

EE

LV

SI

HR

NL

FR

2nd gen.

1st gen.

all migrants

-10

-5

0

5

10

15

20

Note:  Only  MS  and  categories  with  statistically  significant  differences  are  shown.  MS  ordered  according  to 

change in all immigrants. 
Source: based on OECD (2016c). 

In addition, Figure 9 shows the share of immigrants participating in PISA 2015 as well as 
the gap (and its change between 2006 and 2015) in science scores between natives and 
migrant  students.  In  most  MS  natives  have  a  significantly  higher  score  than  migrants, 
even  after  accounting  for  socio-economic  status  and  language  spoken  at  home.  The 
largest gap exists in BG, followed by SE, SK, DK and FI, all with a gap corresponding to 
about one year difference of schooling and more.13 Comparing 2006 to 2015 results also 
mostly indicates that there have been no statistically significant changes in these gaps in 
most MS, apart from a few MS in which the gap has become smaller. This is the case in 
BE, ES, IT, LU and PT.  

13 See similar interpretations of score point differences in OECD (2010, p. 30) and OECD (2016e, p. 7). 

17 

 
 
 
                                           
Figure 9. Science score gaps and shares of immigrants in PISA 

60

40

20

s
e
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s
A
S
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P

0

-20

-40

-60

AT BG BE DE DK EE

EL

ES

FI

FR HR IT

LU LV NL PT SE

SK UK

60

40

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00

-20

-40

-60

s
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%

5
1
0
2

A
S
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P

n

i

PISA score gap in 2015

change in PISA score gap, 2006-2015

share of immigrant students in 2015

Note:  PISA  score  gaps  in  science  shown  are  between  natives  and  immigrant  students,  after  accounting  for 

socio-economic status and language spoken at home. Only significant values are shown. 

Source: based on OECD (2016c).  

Science  score  results  also  vary  substantially  among  MS,  even  if  the  origin  countries  of 
migrants  are  the  same.  For  example,  Figure  10  shows  that  it  makes  an  important 
difference  whether  an  Arabic-speaking  student,  both  in  the  first  or  second  generation, 
goes to school in NL, FI or DK. In fact, in NL these students score always higher than in 
their  Nordic  counterparts.  A  similar  observation  can  be  made  for  Turkish  students,  who 
always  perform  best  in  NL,  and  worst  in  DE  (first  generation)  and  DK  (second 
generation). In comparison to these observations, the differences among Polish students 
in DE, AT and UK are rather minor. 

18 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 10. PISA 2015 science scores for students from various origin countries 

Students from 
Arabic-speaking
countries in:

Second-generation immigrant
students

First-generation immigrant
students

NL

FI

DK

Students 
from Turkey in:

NL

DE

BE

AT

DK

Students 
from Russia in:

FI

LV

CZ

AT

Students 
from BH in:

HR

AT

Students 
from Poland in:

DE

AT

UK

300

350

400

450

500

600

550
Mean score

300

350

400

450

500

550
600
Mean score

Note: BH = Bosnia-Herzegovina. 

Source: adapted from OECD (2016c).  

4.1.2  The intersection of immigrants and educational expectations 

After  these  general  observations  about  the  characteristics  of  immigrant  students  and 
their performance, let us now turn to the more specific question of this brief. That is, the 
relationship between immigrant status and educational attainment, in particular expected 
early  school  leaving.  We  will  present  results  only  for  those  MS  with  a  sufficient  high 
number of migrants and where expected dropout rates are available.  

Considering  Figure  11,  we  can  see  that  in  most  MS  the  shares  of  both  natives  and 
immigrants expecting to leave school earlier (i.e., the shares of those who plan to finish 

19 

 
 
 
education at ISCED 2) are quite low. High shares of expected ESL exist in systems with 
relatively  important  shares  of  vocational  education,  that  is  DE,  DK,  FI  and  NL  (see  also 
appendix). But also in MS like ES, FR and PT we can see relevant expected ESL shares.  

Figure 11. Shares of expected educational levels, considering natives and immigrants 

n
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Native - ISCED 2
Native - ISCED 3-4
Native - ISCED 5-6

Migrant - ISCED 2
Migrant - ISCED 3-4
Migrant - ISCED 5-6

Note:  Shares  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

In addition to simple shares, it is also interesting to consider whether students that have 
low educational expectations also perform worse than others. In fact, one would expect a 
lower  performance,  i.e.  lower  PISA  scores,  for  expected  ESL  as  they  do  not  have  an 
incentive to stay longer in school, and often have had difficulties in school. To understand 
this  issue  better,  we  compute  separate  scores  for  ESL  and  non-ESL,  for  natives  and 
immigrants  (distinguishing  also  first  and  second  level  generations).  In  many  cases, 
sample  size  is  insufficient  for  constructing  scores  for  all  categories  (i.e.,  separately  for 
natives,  first  and  second  generation).  As  expected,  we  find  that  –  where  all  categories 
can  be  computed  –  ESL  have  a  lower  performance  than  non-ESL  in  all  cases  (i.e.  the 
score point differences are always positive; see Figure 12). These differences amount to 
more than one year of schooling, in many cases even more than two years of schooling. 
The differences are in some MS largest among natives, such as DE and FR. In contrast, 
differences  are  largest  among  first  generation  migrants  in  DK  and  LU,  while  they  are 
never largest among second generation students.  

20 

 
 
 
 
Figure 12. Gap between expected non-ESL and expected ESL in PISA 2015 

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120

100

80

60

40

20

0

AT BE BG CY DE DK EE EL ES

FI

FR HU IE

IT LT LU LV NL PL PT SE

SI UK

Natives

Second-Generation

First-Generation

Note:  Scores  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

In  a  next  step,  we  consider  only  expected  non-ESL,  and  distinguish  among  natives  and 
the  various  immigrant  groups  (see  Figure  13).  Among  expected  non-ESL,  we  find  large 
gaps  between  natives  and  first-  and  second-generation  immigrants  in  many  MS,  while 
they  are  in  many  cases  not  as  large  as  in  the  previous  case.  Still,  they  can  be  up  to 
around 90 score points or more than two years of schooling (in SE and SI). Apart from a 
few cases, in which second generation students (in CY, CZ, HU and LT) or first generation 
students (HU) have higher scores than natives among expected non-ESL, natives always 
outperform immigrants. The tendency is also that the gap between natives and second-
generation  immigrants  is  smaller  than  between  natives  and  first-generation  migrants. 
This  is  intuitive  as  second-generation  immigrants  have  less  difficulty  in  integrating  into 
the  host  country  and  learning  a new  language,  as  they  were  born  in the  same  country. 
The reverse case is notably present in DK, and to a smaller extent in BE, IE and LU. 

21 

 
 
 
 
 
Figure 13. Gap among expected non-ESL between natives and immigrant groups 

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0

-20

-40

AT BE CY CZ DE DK EE EL ES

FI

FR HR HU IE

IT LT LU LV NL PT SE SI UK

Natives and Second-Generation

Natives and First-Generation

Note:  Scores  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

Figure  14  provides  the  same  analysis,  but  this  time  looking  at  the  differences  between 
natives  and  immigrant  groups  among  expected  ESL.  Interestingly,  the  score  point 
differences  are  mostly  in  favour  of  natives,  particularly  in  DE  and  DK.  In  contrast,  very 
small differences exist in CY and IE. In the four MS where we have enough observations 
for  expected  ESL  among  both  first  and  second  generation  immigrants  (i.e.,  in  DE,  DK, 
FR,  and  LU),  we  always  find  that  second  generation  students  have  lower  score  point 
differences (compared to first generation ones), confirming the previous results.  

22 

 
 
 
 
 
Figure 14. Gap among expected ESL between natives and immigrant groups 

e
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80

70

60

50

40

30

20

10

0

-10

CY

DE

DK

EE

ES

FR

IE

LU

NL

SE

Natives and Second-Generation

Natives and First-Generation

Note:  Scores  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

Finally,  we  also  consider  the  differences  between  natives,  EU  migrants  and  non-EU 
migrants. For this reason, we repeat the same procedure as for the previous graphs, but 
instead  of  distinguishing  between  first  and  second  generation  migrants,  we  now 
distinguish  between  EU  migrants  and  non-EU  immigrants  (where  sample  size  is 
sufficiently  high).  Thus,  in  fact,  only  in  LU  we  can  compare  all  three  categories.  In  this 
MS,  we  find  that  natives  and  EU  migrants  have  almost  the  same  score  point  difference 
among  expected  non-ESL  and  ESL,  while  in  the  case  of  non-EU  immigrants  it  is  a  bit 
lower. In general, we find that expected ESL have a lower performance than non-ESL in 
all  cases  (see  Figure  15),  confirming  our  previous  results.  Differences  among  non-EU 
immigrants  are  usually  lower  than  among  natives,  except  for  FI.  In  contrast,  the 
differences  among  EU  migrants  are  about  an  entire  year  of  schooling  higher  than  for 
natives in DE, the only MS where we can compare these categories.  

23 

 
 
 
 
 
 
Figure 15. Gap between expected non-ESL and expected ESL in PISA 2015, EU and non-EU 

e
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140

120

100

80

60

40

20

0

AT BE BG CY DE DK EE EL ES
Natives

FI

FR HU IE
EU migrants

IT LT LU LV NL PL PT SE SI UK

non-EU immigrants

Note:  Scores  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

Second, we now consider only expected non-ESL (see Figure 16). Among expected non-
ESL,  we  find  large  gaps  between  natives  and  EU  and  non-EU  immigrants  in  many  MS. 
They are up to 80 score points or around two years of schooling (in FI). In a few cases, 
EU  migrant  students  (in  CY,  IE  and,  most  strikingly,  PT)  or  non-EU  immigrant  students 
(HU) have higher scores than natives among expected non-ESL. However, in most cases 
natives outperform immigrants. The gap between natives and EU migrants appears to be 
smaller than the gap between natives and non-EU immigrants. This is an intuitive result 
as EU migrants may have on average a higher socio-economic background than non-EU 
immigrants.  Nevertheless,  we  find  the  opposite  situation  in  LU,  and  to  a  smaller  extent 
also in CZ and UK. 

24 

 
 
 
 
 
Figure 16. Gap among expected non-ESL between natives and immigrant groups, EU and non-EU 

e
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80

60

40

20

0

-20

-40

AT BE BG CY CZ DE DK EE EL ES FI FR HR HU IE IT LT LU LV NL PT SE SI UK

Natives and EU migrants

Natives and non-EU immigrants

Note:  Scores  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

Figure  17  considers  the  differences  between  natives  and  immigrant  groups  among 
expected  ESL.  In  most  cases,  the  score  point  differences  are  high  in  favour  of  natives, 
particularly  again  in  FI  (compared  to  non-EU  immigrants,  it  is  almost  three  years  of 
schooling).  However,  only  small  differences  exist  in  ES  and  IE  (in  the  latter  MS,  the 
difference  with  non-EU  immigrants  is  virtually  zero).  Only  in  DE  and  LU  we  are  able  to 
compare expected ESL among both EU and non-EU immigrants. We find in both MS that 
EU migrant students have a  score point difference (with respect to natives) higher than 
the one for non-EU immigrant students. Interestingly, however, non-EU immigrants have 
higher scores than natives among expected ESL in LU. 

25 

 
 
 
 
 
Figure 17. Gap among expected ESL between natives and immigrant groups, EU and non-EU 

140

120

100

e
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p
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80

60

40

20

0

-20

BE

DE

DK

EE

ES

FI

FR

IE

IT

LU

NL

SE

Natives and EU migrants

Natives and non-EU immigrants

Note:  Scores  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

4.1.3  Summary descriptives of variables  

Table 3 below provides descriptive statistics and sample size for the variables used in the 
multilevel  model.  As  we  are  looking  at  country-level  data,  we  have  more  than  120,000 
observations for all MS included.  

At the student level, expected dropout varies significantly – that is, while the mean value 
of expected dropout is 2.35, its standard deviation is 14.66. 

Table 3. Descriptive statistics for the variables included in this study 

Variable  
Expected early school leaving 
Gender 
Student's ESCS 
1st generation 
2nd generation 
Language at home 
Age at arrival 
Sense of belonging 
Epistemological beliefs 
Truancy 
Grade repetition 
(Programme) Designation 
(Programme) Orientation  
ISCED Level 

Mean 
2.35 
0.50 
-0.01 
0.53 
0.55 
0.57 
1.70 
5.59 
16.83 
-0.20 
0.56 
1.46 
1.30 
2.56 

SD 
14.66 
0.50 
0.95 
2.01 
2.01 
6.66 
11.04 
22.66 
37.17 
0.81 
1.86 
0.79 
0.70 
0.50 

N 
124932 
127020 
123713 
126834 
126834 
124971 
124884 
126684 
126271 
124317 
126885 
127020 
127020 
127020 

26 

 
 
 
 
 
The  explanatory  variables  also  vary  significantly,  which  provides  a  basis  for  analyzing 
school  differences  in  expected  student  dropouts  using  this  set  of  explanatory  variables. 
The majority of the variables have no missing data. 

4.2

  Multilevel regression analysis 

4.2.1  For the entire EU 

Multilevel  regression  models  are  well-suited  to  the  analysis  of  educational  data.  They 
recognize  the  hierarchical  structure  of  the  data  with  students  nested  in  schools  and 
countries.  In  this  analysis,  the  two-level  model  reflects  that  students  are  nested  in 
schools. The associations between educational expectations and student-level or school-
level  factors  can  be  decomposed  into  within-school  and  between-school  associations. 
Thus,  with  these  models  it  is  possible  to  analyse  how  differences  in  key  policy-relevant 
variables are associated with student educational expectations. 

Table 4 shows the results from the two-level logit regression with school random effects 
and  students  being  nested  in  schools,  including  the  variables  that  we  have  discussed  in 
the  previous  section.  The  model  estimates  the  probability  of  having  lower  expectations 
(finishing school at ISCED 2) as a function of a set of student- and school-level factors. 
The  model  is  representative  of  the  whole  population  of  15-year-olds  in  MS  with  a 
sufficient  number  of  immigrant  students.  As  mentioned  before,  using  the  criterion  for 
PISA  data  developed  by  the  OECD,  MS  with  less  than  30  immigrant  students  or  with 
immigrant students sampled from less than 5 schools are excluded from the analysis. As 
a result, we disregard Poland and Romania. In addition, data from Malta and Slovakia are 
not  analysed  as  these  countries  did  not  provide  information  on  student  educational 
expectations.  

Column (1) presents the model with the full set of predictors without the science scores 
for students and  schools. Column (2) presents a similar model but with student science 
scores and school average science scores added. Thus, the results in column (2) provide 
coefficients after controlling for the actual student and school performance in science.  In 
general,  both  sets  of  results  provide  similar  insights.  In  both  models,  country  fixed 
effects  are  included.  Except  for  the  immigrant  variables,  here  we  focus  on  presenting 
only significant associations. 

As the main focus of our study is on immigrant students, we begin with the discussion of 
the  variables  that  are  specifically  related  to  this  group  (i.e.,  language  at  home,  age  at 
arrival,  first  generation,  second  generation).  Interestingly,  we  do  not  find  that  these 
variables are significant. At the lower end of the regression table we have also included a 
number  of  interaction  terms  between  immigrant  status  and  other  variables,  which  are 
also all insignificant. Thus, it appears that immigrants may not be ‘special’ or inherently 
different  in  their  expected  dropout  behaviour  with  regard  to  natives.  Both  immigrants 
and  natives  behave  in  the  same  way  when  forming  their  educational  expectations, 
influenced by a number of common factors that we control for.  

The  variable  reflecting  student  socio-economic  background  included  in  our  models  is 
typically used in the analysis of PISA data and the estimated coefficients are also in line 
with other research. Student socio-economic background as measured by the PISA ESCS 
index is negatively related to expected early dropout at the student level (but not at the 
school  level).  This  result  reflects  the  strong  association  between  socio-economic 
background and educational expectations that is observed across many countries.  

The  results  from  the  multilevel  model  also  suggest  that  epistemological  beliefs  are 
negatively  related  to  the  lower  educational  expectations.  However,  their  strong 
relationship with scores leads to the fact that the effect disappears once students' scores 
are included. Still, these results seem to directly refer to policies that aim at changing the 

27 

way  scientific  knowledge  is  presented  to  students.  Stipulating  better  understanding  of 
scientific knowledge might affect expectation levels and should hence  be considered as a 
policy option in reforming curricula and the way science is taught in schools. 

In  our  multilevel  model,  grade  repetition  is  associated  with  higher  expected  dropout 
probability, which can be explained by the fact that students repeating a grade are those 
who in fact struggle with learning more than their peers, and thus are more susceptible 
to  leave  school  early.  In  some  MS,  however,  grade  repetition  is  rare  despite  that  these 
MS  might  face  similar  challenges  in  terms  of  students'  low  performance  or  socio-
economic background. This makes us think that there might be room for rethinking grade 
repetition practices in some MS.  

A  school’s  expected  ESL  level  is  positively  significantly  related  to  expected  early  school 
leaving at the student level. This finding implies that compositional effects at the school 
level play a role. In other words, there may be peer effects of many students wanting to 
leave school early in a particular school.14 Another possibility is segregation if in certain 
schools there is a higher proportion of low achievers. 

Finally,  if  a  student  is  already  in  ISCED3,  the  odds  of  early  dropout  (i.e.  finishing  after 
ISCED2) are lower than for ISCED2 students. This result is intuitive, as such a student is 
already attending a higher school level. 

Comparing the two models in columns (1) and (2), we see that the overall results remain 
intact  no  matter  if  science  scores  at  the  student  and  the  school  level  are  included. 
However,  these  two  variables  themselves  are  both  significant  negative  predictors  of 
expected early school dropout. These  results indicate the plausible relationship between 
having  a  higher  (science)  score  and  the  willingness  to  study  further,  or  in  other  words 
that students that score lower have a higher probability to expect to leave school early.  

Overall,  one  important  conclusion  is  that  both  individual  (e.g.  epistemological  beliefs,  a 
student’s  ESCS)  and  compositional  effects  (a  school’s  expected  early  dropout  rate  and 
average  science  score)  play  an  important  role,  so  that  also  segregation  of  students 
increases  dropout  probability.  The  fact  that  most  of  the  migrant-related  variables  are 
insignificant is in line with other research, which indicates that being a migrant is not the 
driving force behind the probability to leave school early (e.g., European Commission et 
al.,  2014).  Instead,  a  key  factor  seems  rather  to  be  the  situation  that  a  student  and  a 
student’s  family  are  in  and  the  effects  of  schools  characteristics  on  students’ 
expectations.  

14  As  for  the  programme  orientation,  students  in  pre-vocational  education  have  on  average  a  lower  expected 
early school leaving probability than students in general education. However, one has to bear in mind that 
it is a country-specific variable as it is coded this way only in few MS. Thus, we do not discuss it here but 
only when we consider individual MS in the next subchapter. 

28 

 
 
  
 
 
 
                                           
Table 4. Multilevel regressions on expected early school leaving for the entire EU 

(1)

(2)

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

(0.101)

0.055
(0.101)

(0.049)
-0.317
(0.340)
-0.176
(0.190)
0.161
(0.127)
0.052
(0.036)
-0.016
(0.044)
-0.163***
(0.039)
0.033
(0.043)

-0.060
(0.091)
-0.404*** -0.348***
(0.048)
-0.342
(0.364)
-0.325
(0.193)
-0.006
(0.147)
0.029
(0.038)
-0.023
(0.043)
-0.066
(0.036)
-0.030
(0.044)
1.035*** 0.767***
(0.100)
-0.646*** -0.644***
(0.176)
0.264
(0.260)
0.596
(0.388)
-0.669*
(0.275)
0.081
(0.219)
0.431*
(0.214)
-0.335
(0.263)
0.040
(0.106)
-0.060
(0.074)
0.246
(0.183)
5.572*** 5.656***
(0.208)
-0.003***
(.)
-0.007***
(0.001)
-3.774*** -2.061***
(0.528)

(0.167)
0.364
(0.237)
0.728*
(0.367)
-0.436
(0.245)
0.153
(0.212)
0.885***
(0.222)
-0.441
(0.242)
0.058
(0.105)
-0.138*
(0.063)
0.061
(0.165)

(0.269)

(0.176)

Note: Significance level: * p<0.05, ** p<0.01, *** p<0.001. Results from multilevel regression with random 
effects.  Estimates  over  ten  imputed  datasets  after  multiple  imputation  of  missing  data  for  each  dataset 
containing one of the ten plausible values of student achievement. The variables starting with ‘I:’ indicate 

29 

 
 
interaction  terms.  The  variable  ‘Designation’  has  the  following  categories:  “A”  (general  programmes 
designed  to  give  access  to  the  next  programme  level);  “B”  (programmes  designed  to  give  access  to 
vocational  studies  at  the  next  programme  level);  “C”  (programmes  designed  to  give  direct  access  to  the 
labour market). For more detailed information on all variables, see appendix. For the reference categories, 
see Table 1.  

4.2.2  Country by country 

In a second step, we analyse whether there exist relevant differences among MS that are 
hidden  in  the  overall  results  for  the  entire  EU.  Therefore,  we  run  the  same  multilevel 
regressions at the country level for each MS, once without students’ and schools’ science 
scores (indicated by the ending “_”, e.g.,  “AT_” for Austria) and once with these scores 
(ending  in  “_pv”,  e.g.  “AT_pv”).  In  addition,  we  also  include  standard  logit  regressions 
(indicated by “_logit”, e.g. “AT_logit”) to provide an additional robustness  check  for  our 
results. We only provide a brief summary here, but the details on the country regressions 
can be found in the appendix in Table 7.  

Overall, the results for these multilevel regressions confirm our multilevel models for the 
entire EU, in the sense that similar variables are significantly related to outcomes. In the 
country-level  regressions,  the  student-level  variables  "ESCS",  "epistemological  beliefs", 
"grade repetition" and "science score", and the school-level variable "expected ESL level" 
turn out to be often significantly and with the same signs found in the overall sample. In 
fact,  the  only  variable  that  is  always  significant  in  all  MS  and  across  all  specifications 
(including logit models) is the school’s expected ESL level. That is, it is always positively 
related  to  a  student’s  expected  ESL  probability.  This  finding  appears  to  indicate  that 
there  are  relevant  school  or  peer  factors  within  schools  that  have  a  major  influence  on 
the  expected  early  dropout  behaviour  of  students.  Thus,  there  seems  to  be 
interdependence between the expectations of individual students and those  of the other 
students within the same school.  

The  inclusion  of  the  student’s  science  score  in  the  “_pv”  models  has  as  a  consequence 
that the coefficients of other variables are less often significant, showing the relevance of 
this factor. This is e.g. the case for epistemological beliefs but also for grade repetition – 
not  a  surprising  result,  given  that  these  factors  have  been  found  to  be  significantly 
associated to science performance in other related research on PISA 2015 (Hippe et al., 
2017;  OECD,  2016c).  Also,  it  is  not  surprising  that  lower  science  performance  is 
associated  with  lower  educational  expectations.  It  only  shows  that  students  rationally 
evaluate their own capabilities and that motivation is intrinsically connected with science 
results.  

In  addition,  in  the  models  with  student  scores  included,  the  regressions  show  that 
immigrant status does matter in only in few MS. In other words, the coefficients for being 
a  first  generation  and  being  a  second  generation  immigrant  student  are  negatively 
significant in 4 MS (out of the 23 MS included). It is a minority of MS, which may explain 
why  it  is  not  a  significant  factor  in  the  regressions  for  the  entire  EU.  Furthermore,  the 
effect  of  being  a  first  or  second  generation  immigrant  student  changes  across  MS:  in 
some cases it is positive while in most it is negative. This appears to be in line with our 
descriptive results earlier on, which emphasise the varying score point differences in the 
various MS.  

Moreover,  students’  sense  of  belonging  to  a  school  is  negatively  related  to  expected 
dropout probability in a relevant number of MS. This finding indicates that the feeling of 
being  integrated  and  accepted  by  school  mates  and  a  student’s  identification  with  its 
learning environment is a relevant factor in some MS.  

Of course, the picture is even more complex when considering all variables for all MS, but 
this detailed analysis goes beyond the limits of this technical brief. We provide, however, 
some additional insights in the appendix. Those results indicate that in each MS there are 
different  variables  that  have  an  additional  important  role  in  relation  to  expected  early 
dropout.  This  shows  the  diversity  of  the  European  education  systems  and  also  of 

30 

immigrant populations in the MS. Further research would be needed to better understand 
the cross-country differences and their relationship with MS specificities. 

4.2.3  Distinction among EU and non-EU immigrants 

Comparison of EU and non-EU immigrants to natives 

In  this  section  we  explore  whether  there  are  relevant  differences  in  the  early  dropout 
probabilities  when  separately  comparing  EU  and  non-EU  immigrant  students  to  native 
students.  Thus,  we  run  the  same  set  of  regressions  for  these  two  different  immigrant 
groups with native students as the baseline category.  

Looking  at  all  MS  together  (see  Table  8  in  the  appendix),  we  find  that  both  first  and 
second  generation  EU  migrants  do  not  have  a  different  early  dropout  probability  than 
natives.  The same is also true for non-EU immigrants (see Table 9 in the appendix).  

Similar to previous results, the significant negative coefficient on epistemological beliefs 
(as compared to natives) disappears once students’ science scores are included (compare 
columns  1  and  2  in  Table  8  and  Table  9).  Our  results  suggest  that  the  school's 
concentration  of  immigrants  leads  to  higher  early  dropout  probability  for  EU  migrants, 
while not for non-EU immigrants. 

Moreover, the average science score of the school level (and thus the school background) 
matters for both EU and non-EU immigrants as we find a significant negative effect of a 
school’s  science  score.  Thus,  it  seems  that  school  composition  (or  peer  effects)  shapes 
educational expectations. The additional detailed country-by-country results can be found 
in the appendix (see Table 10 and Table 11). 

Direct comparison of EU to non-EU immigrants 

In a final step, we directly compare EU and non-EU immigrants and the factors that lead 
to  different  expected  early  dropout  probabilities  for  these  two  groups  (i.e.  we  drop  all 
native students from the regressions; see also detailed explanations in the appendix). In 
other words, we want to know whether there are differences in the factors associated to 
expected  early  school  leaving  between  EU  migrants  and  non-EU  immigrants.  Evidently, 
sample  sizes  are  small,  so  results  should  be  taken with  caution.  For  all  EU  MS  together 
(see  appendix,  Table  12),  after  controlling  for  students’  science  scores  and  taking  EU 
migrants  as  the  reference  category,  we  find  that  the  sense  of  belonging  is  negatively 
significant,  indicating  that  the  sense  of  belonging  lowers  significantly  the  early  dropout 
probability  for  EU  migrant  students.  This  confirms  earlier  results  from  comparisons 
between native students and EU migrants.  

We  also  find  that  grade  repetition  is  positively  significantly  related  to  expected  early 
dropout  in  the  case  of  EU  migrants.  This  finding  shows  again  that  grade  repetition 
practices  are  particularly  affecting  the  early  dropout  expectations  of  EU  migrants.  We 
didn’t  find  any  other  significant  differences  between  EU  and  non-EU  migrants  in  the 
sample  with  pooled  data  from  all  MS.  Further  regression  analysis  conducted  separately 
for each MS can be found in the appendix (see appendix, Table 13).  

5  Conclusions  

This  technical  brief  has  considered  educational  expectations  among  various  student 
populations  in  Europe,  in  particular  distinguishing  between  native  and  immigrant 
students.  Educational  and  skills  differences  are  seen  in  the  literature  as  fundamental 
factors  for  economic  divides  (Hanushek  &  Wößmann,  2015).  Understanding  better 
educational  divergences  in  a  European  perspective  and  subsequently  improving 
educational attainment may help in countering widening divides within and across MS. 

31 

 
 
 
 
The  recent  refugee  crisis  has  put  even  more  to  the forefront  the  issue  of  immigrants in 
the  EU  agenda  (European  Commission,  2016),  while  migration  patterns  among  EU  MS 
have  led  to  discussions  in  both  origin  and  destination  countries.  At  the  same  time,  the 
Europe 2020 headline target of achieving less than 10 % early school dropouts shows the 
importance that policy makers give to the reduction in ESL. ESL reflects and reinforces a 
range  of  individual  and  social  disadvantages,  which  have  negative  consequences  for 
students, the society and the state.  

In  this  study,  we  have  used  OECD’s  PISA  2015  data  to  explore  the  importance  of 
immigrant  status  –  together  with  other  factors  measured  at  the  school  and  individual 
level  – in  accounting  for  the  probability  of  early  school  leaving.  PISA  provides  the  most 
employed  data  in  the  field  of  educational  achievement.  In  particular,  PISA  2015  data 
have the advantage that they include more variables than previous rounds. PISA data on 
migrants  (irrespective  from  their  origin  within  or  outside  the  EU)  are  available  for  the 
majority of MS, but not for all. Similarly, there is no information included on educational 
expectations  of  students  for  a  few  MS,  further  reducing  the  usable  sample.  In  addition, 
we  also  employ  data  from  Eurostat  to  complement  the  picture  on  early  school  leavers 
and immigrants. 

We analyse expected early school leaving in PISA in various ways. First, we present some 
descriptive  statistics  to  get  a  better  intuition  for  the  data.  Then,  we  run  two-level 
multilevel  models  including  both  the  student  and  the  school  level.  The  employed 
explanatory  variables  follow  the  standard  methodology  by  the  OECD  and  include  new 
relevant variables that are introduced in PISA 2015 for the first time. In addition, we also 
constructed a new variable on truancy from PISA responses. 

Overall,  in  various  settings  we  find  that,  once  we  control  for  individual  and  school 
characteristics,  immigrant  status  does  not  have  a  significant  effect  on  the  expected 
probability  of  early  school  leaving.  The  latter  depends  rather  on  other  factors,  that  are 
unequally distributed between migrants and natives (socio-economic status in particular), 
but  that  affect  similarly  both  groups.  In  particular,  our  analyses  indicate  that  the  most 
relevant  factors  that  decrease  the  likelihood  of  expected  early  school  leaving  are  the 
ESCS  of  students  and  their  epistemological  beliefs,  while  grade  repetition  increases  it. 
However,  the  importance  of  these  student  factors  varies  to  various  degrees  among  MS. 
Interestingly, consistent among all MS is the positive association of a student’s expected 
ESL and the share of expected early school leavers at the school level. This result implies 
that the school environment and peer effects are strong and have a significant impact on 
the dropout decisions of students, independent of whether they are native or immigrant.  

In  particular,  there  might  be  space  to  revise  grade  repetition  practices  in  MS,  and  our 
results give further emphasis to the specific recommendation related to grade repetition 
by  the  Thematic  Workgroup  on  Early  School  Leaving  (European  Commission,  2016). 
Moreover,  our  results  are  in  line  with  the  literature  as  concerns  the  socio-economic 
background (ESCS). The ESCS is a major predictor of expected early school leaving. This 
means that students with a low socio-economic background have the tendency to expect 
to  leave  school  earlier  than  those  who  come  from  higher  socio-economic  strata. 
Therefore, it is important that education policy aim at equalising opportunities across the 
different layers of society.  

While  males  have  usually  higher  ESL  rates,  we  do  not  find  that  gender  is  a  significant 
factor across  Europe  when a whole set  of  explanatory variables is included. Even at the 
country  level,  gender  is  only  rarely  significant,  and  the  direction  of  the  effect  does  vary 
(i.e.,  it  is  in  part  positive  or  negative).  Thus,  our  study  suggests  that  there  may  be 
differences  between  boys  and  girls  but  these  are  related  to  other  factors  that  are 
common to both sexes.  

On  the  other  hand,  it  would  be  advisable  to  analyse  for  each  school  what  leads  to  the 
dropout  decisions  of  students,  and  how  this  can  be  improved.  For  example,  providing 
more information for students about possible future educational and career opportunities 
may  be  a  potential  useful  policy  to  tackle  low  educational  expectations.  As  the  OECD 

32 

 
(2017b, p. 111) notes,  “[s]chools  should provide academic and career counselling to all 
students  so  that  they  develop  ambitious  –  yet  realistic  –  expectations  about  their 
education  and  career  prospects”.  In  addition,  our  country-by-country  results  also 
emphasise  the  relevance  of  the  sense  of  belonging  of  students.  The  relevance  of 
students’ sense of belonging to a school in a number of MS gives further credence to the 
importance  of  helping  students,  with  native  or  immigrant  background,  to  provide  a 
learning-conducive  environment  and  to  take  into  account  the  specific  needs  of  each 
student (see also European Commission, 2013b). In consequence, measures that aim at 
improving the school climate could be useful in this regard.  

Policy makers can take away a number of further messages from this study. In fact, the 
most  important  limitation  of  this  study  is  sample  size.  Data  on  immigrant  students  are 
quite limited for many MS. This is even more the case when distinguishing among various 
immigrant subpopulations (i.e., first generation and second generation, as well as EU and 
non-EU  immigrants).  The  same  lack  of  information  exists  for  early  school  leavers.  More 
generally,  this  fact  has  already  been  indicated,  particularly  for  the  latter  group,  by  the 
European Commission (2016, p. 4), stating that “with some notable exceptions, Member 
States lack detailed information on the background of early school leavers and analyses 
of  the  causes  and  incidence  of  early  school  leaving.  Only  a  few  countries  take  a 
systematic  approach  to  collecting,  monitoring  and  analysing  data  on  early  school 
leaving”.  This  study  aims  to  reduce  the  knowledge  gap  and  better  understand  the 
characteristics of (expected) early school leavers. PISA provides the information that we 
have  exploited  in  a  comparable  way  across  MS,  providing  a  European  and  international 
perspective  on  these  issues.  Nevertheless,  evidence-based  policy  making  is  still  lacking 
for  many  MS.  More  data  collection  and  analysis  is  needed  for  the  design  and 
implementation  of  more  efficient  policies.  Therefore,  more  data  on  these  student 
subpopulations  need  also  to  be  collected  in  future  PISA  rounds,  as  our  analyses  clearly 
show the limitations of the available data. 

Similarly, a holistic approach to the challenge of early school leavers and the successful 
integration  of  immigrant  students  in  schools  and  society  is  needed  to  create  efficient 
education policies in this area. This holistic approach would not only consider the specific 
groups  at  stake,  but  include  a  larger  set  of  stakeholders  at  the  local  and  regional  level 
and take into account all parts of the education system in a comprehensive strategy (see 
also  recommendations  by  European  Commission  2013b,  2016).  In  many  MS,  such  a 
national strategy is still not designed (European Commission, 2016). 

Finally, the results of this and other (e.g., Hippe et al., 2017) studies indicate that in the 
future  it  may  be  worth  studying  more  in  detail  how  epistemological  beliefs  are  created, 
as  this  is  a  new  item  in  PISA  2015  and  highly  related  to  early  school  leaving  and  PISA 
scores.  In other words, what are the factors that shape epistemological beliefs and how 
can  these  factors  be  influenced  by  policy?  Understanding  these  issues  better  could  be 
very useful in improving both educational attainment and educational achievement rates 
in the MS.  

33 

 
 
 
 
 
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38 

List of abbreviations and definitions 

EU MS abbreviations 

Code 
AT 
BE 
BG 
CY 
CZ 
DE 
DK 
EE 
EL 
ES 
FI 
FR 
HR 
HU 
IE 
IT 
LT 
LU 
LV 
MT 
NL 
PL 
PT 
RO 
SE 
SI 
SK 
UK 

MS name 
Austria 
Belgium 
Bulgaria 
Cyprus 
Czechia 
Germany 
Denmark 
Estonia 
Greece 
Spain 
Finland 
France 
Croatia 
Hungary 
Ireland 
Italy 
Lithuania 
Luxembourg 
Latvia 
Malta 
The Netherlands 
Poland 
Portugal 
Romania 
Sweden 
Slovenia 
Slovakia 
United Kingdom 

Non-EU country abbreviations 

Code 

Country name 

AL 
BH 
CH 
GE 
IS 
KS 
MD 
ME 
MK 
NO 
RS 
RU 
TR 

Albania 
Bosnia-Herzegovina 
Switzerland 
Georgia 
Iceland 
Kosovo 
Moldova 
Montenegro 
FYROM 
Norway 
Republic of Serbia 
Russian Federation 
Turkey 

39 

Other abbreviations 

Abbreviation 
Coef. 
ESCS 
ESL 
EU 
GDP 
Gen. 
IRT 
ISCED 
MS 
OECD 
PISA 
S.E. 

Description 
Coefficient 
Economic, Social and Cultural Status 
Early school leaver(s) 
European Union 
Gross Domestic Product 
Generation 
Items Response Theory 
International Standard Classification of Education 
Member States 
Organisation for Economic Co-operation and Development 
Programme for International Student Assessment 
Standard Errors 

40 

List of figures 

Figure 1. Shares of ESL in Europe, 2016 ................................................................. 1 

Figure 2. Explanatory variables used in the analysis ................................................. 7 

Figure 3. Share of immigrants in total population in European countries, 2016 ............12 

Figure 4. Share of EU migrants among all immigrants in European countries, 2016 .....13 

Figure 5. Share of immigrant students in PISA 2015 ................................................14 

Figure 6. Share of first generation among immigrant students in PISA 2015 ...............15 

Figure 7. Share of EU students among immigrant students in PISA 2015....................16 

Figure 8. Percentage change in number of immigrants between 2006 and 2015 ..........17 

Figure 9. Science score gaps and shares of immigrants in PISA .................................18 

Figure 10. PISA 2015 science scores for students from various origin countries ..........19 

Figure 11. Shares of expected educational levels, considering natives and immigrants.20 

Figure 12. Gap between expected non-ESL and expected ESL in PISA 2015 ...............21 

Figure 13. Gap among expected non-ESL between natives and immigrant groups .......22 

Figure 14. Gap among expected ESL between natives and immigrant groups .............23 

Figure 15. Gap between expected non-ESL and expected ESL in PISA 2015, EU and non-
EU ......................................................................................................................24 

Figure 16. Gap among expected non-ESL between natives and immigrant groups, EU 
and non-EU .........................................................................................................25 

Figure 17. Gap among expected ESL between natives and immigrant groups, EU and 
non-EU ................................................................................................................26 

Figure 18. Europe 2020 targets for early school leaving and current rates ..................43 

Figure 19. Evolution of ESL rates towards Europe 2020 targets, by MS, 2012-2015 .....44 

Figure 20. Evolution of ESL rates towards Europe 2020 targets, by MS, 2009-2012 .....45 

Figure 21. Evolution of ESL rates towards Europe 2020 targets, EU level, 2002-2016 ..46 

Figure 22. Duration of compulsory schooling in Europe ............................................47 

Figure 23. Index of current concentration of immigrants in schools ...........................57 

Figure 26. Resilient students in PISA 2015 .............................................................60 

41 

List of tables 

Table 1. Description of student-level variables ......................................................... 9 

Table 2. Sample size by immigrant status ...............................................................10 

Table 3. Descriptive statistics for the variables included in this study .........................26 

Table 4. Multilevel regressions on expected early school leaving for the entire EU ........29 

Table 5. Immigration policies in EU Member States, 2008-2016 ................................49 

Table 6. Number of immigrants in PISA 2015 sample, ISCED 2 students only .............56 

Table 7. Multilevel regressions, country-by-country .................................................63 

Table 8. Multilevel regressions, all MS, for EU migrants ............................................71 

Table 9. Multilevel regressions, all MS, for non-EU migrants ......................................72 

Table 10. Multilevel regressions, country-by-country, for EU migrants .......................73 

Table 11. Multilevel regressions, country-by-country, for non-EU migrants .................76 

Table 12. Multilevel regressions, all MS, baseline category: EU migrants ....................80 

Table 13. Multilevel regressions, country-by-country, baseline category: EU migrants .81 

42 

Appendix 

Annex 1. Europe 2020 targets for early school leaving 

Figure 18. Europe 2020 targets for early school leaving and current rates 

Source: Eurostat  (2017d). 

43 

 
 
 
 
Figure 19. Evolution of ESL rates towards Europe 2020 targets, by MS, 2012-2015 

Source: European Commission  (2016, p. 3).  
Note:  European  Commission    (2016,  p.  3)  explains  the  MS  groupings:  “A.  Countries  that  have  early  school 
leaving  rates  below  or  just  above  10%  and  are  nonetheless  still  making  progress.  These  countries  have 
also reached their national targets, with the exception of Belgium (the UK did not set a national target). B. 
Countries that have early school leaving rates above 10% but are nevertheless making significant progress. 
This  is  a  diverse group  of  countries,  with  Spain  standing out  as  the  Member State  with  the  highest  early 
school  leaving  rate  and  Portugal  among  the  fastest-progressing  Member  States  in  recent  years.  C. 
Countries that have early school leaving rates below 10% but are making little or no progress. Early school 
leaving  rates  have  been  increasing  in  Finland,  Czech  Republic,  Slovenia,  Luxembourg  and  most  notably 
Slovakia, while they declined marginally in Germany, Latvia, The Netherlands, Austria, Sweden and Poland. 
D.  Finally,  the  situation  is  worst  for  the  five  countries  in  Group  D,  combining  early  school  leaving  rates 
above 10% with insufficient progress between 2012 and 2015”. 

44 

 
 
 
 
Figure 20. Evolution of ESL rates towards Europe 2020 targets, by MS, 2009-2012 

Source: European Commission  (2013b).  

45 

 
 
 
 
 
Figure 21. Evolution of ESL rates towards Europe 2020 targets, EU level, 2002-2016 

Source: Eurostat (2017a).  

46 

 
 
 
 
Annex 2. Compulsory schooling in Europe 

Figure 22. Duration of compulsory schooling in Europe  

Source: Eurydice (2015).  

47 

 
 
Annex 3. Definitions of immigrant background 

Non-immigrant (native): born in the country of assessment/survey, or with at least one 
parent born in that country; born abroad with at least one parent born in the country of 
assessment/survey. 

Second-generation  immigrants:  born  in  the  country  of  assessment/survey,  but  whose 
parents  were  born  in  another  country.  In  this  case,  it  is  not  the  individuals  who  move, 
but their parents (definition based on previous generation’s move). 

First-generation  immigrants:  born  outside  the  country  of  assessment/survey  and  whose 
parents were also born in another country.  

EU  migrant:  born  in  an  EU  country  outside  the  country  of  assessment/survey  (first 
generation) or whose parents were born in another EU country while she/he was born in 
the country of assessment/survey (second generation). 

Non-EU immigrant: born in a non-EU country  outside the country  of  assessment/survey 
(first  generation)  or  whose  parents  were  born  in  another  non-EU  country  while  she/he 
was born in the country of assessment/survey (second generation). 

48 

 
 
 
 
 
Annex 4. Recent immigration policies in MS 

Table 5. Immigration policies in EU Member States, 2008-2016 

MS 
Finland 

Policy name  

National  Core  Curriculum 
for 
Instruction  Preparing  Immigrants 
for Basic Education 

Education and Research 

Description 
In  2009,  Finland  introduced  a  reform  to  support 
students with immigration background who are not 
proficient  in  Finnish  or  Swedish.  The  curriculum  is 
differentiated  according  to  students’  age,  learning 
abilities and background.  

A plan to increase the participation of students with 
immigrant  background  in  preparatory  education 
between 2011 and 2016 with the aim of improving 
their opportunity to be enrolled in upper secondary 
education.  In  particular,  in  2014  one  year  of 
preparatory  education  was  created  for  students  to 
integrate general upper secondary education. 

The  Action  Programme  for  Equal 
Opportunity in Education  

The  reform  was  initiated  in  2013  and  aimed  at 
improving the situation of disadvantaged groups at 
all levels of education. 

--- 

The national core curricula for VET implemented in 
Finland 
for 
immigrants. 

includes  educational  arrangements 

Ireland 

Germany  National 

language 

instructional 

Action 
Integration (NAP-I) 

Intercultural Education Strategy   Between  2010  and  2015,  Ireland  implemented  a 
reform  aiming  at  promoting 
“inclusive  and 
intercultural  learning  environments  for  migrant 
students  by  developing  leadership  and  teaching 
quality, 
knowledge, 
mainstreaming,  rights  and  responsibilities  and 
setting  high  expectations,  among  other  features” 
(OECD, 2015a, p. 54). 
In  2011,  in  Germany  there  was  plan  that  “sets 
goals 
training  and  continued 
education to increase the participation and success 
of  students  from  immigrant  backgrounds”  (OECD, 
2015a, p. 54). 
In Slovenia several programmes were instituted to 
support 
low-performing  students  and  schools 
targeting  mainly  students  from  disadvantaged 
socio-economic,  immigrant  or  Roma  backgrounds 
in primary and secondary education. 

in  education, 

Plan 

on 

Slovenia  Liven  Up  the  School  Initiative 
(Popestrimo šolo, 2011) 
Program 
for 
Professionals’  Skills  Improvement 
Integration  of 
for  successful 
Immigrants Students in Education 
(2013) 

Education 

of 

Measures  and  Guidelines  for  the 
integration  of  immigrant  children 
in  kindergartens  and  schools 
(2009 and 2012) 

Strategy  implemented  in  2009  and  amended  in 
2012 aiming at supporting children before the start 
of  school  and  during  their  education.  The  support 
includes parents’ encouragement in participating in 
school  activities  and  also  supporting  schools  in 
planning education. 

Project  raising  the  social  and 
cultural  capital  in  areas  inhabited 
by members of Roma Community 

Project implemented between 2011 and 2013. This 
project  aims  at  increasing  the  participation  and 
success  of  Roma  students  by  introducing  specific 
methods of work with these students.  

Projects 

for 

the  Successful 

Between  2008  and  2016,  Slovenia  implemented  a 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Integration  of  Roma  Students  in 
Schools 

Source: Hippe et al. (2016, pp. 26–28). 

programme  to  share  best  practices  of  inclusive 
teaching  among  kindergartens  and  schools  and 
teachers in areas with little or no such experience. 
this 
Specifically,  between  2013  and  2015, 
programme  provides  educational  activities 
for 
immigrant  students  and  training  for  teaching  staff 
to  promote  interculturalism  in  schools.  Results  of 
this  project  carried  out  “by  the  end  of  2010 
included  higher  attendance  of  Roma  children  in 
improved  co-operation 
educational 
between 
educational 
parents 
institutions,  increased  awareness  among  Roma  of 
the  importance  of  learning  and  education,  and 
more  successful  co-operation  between  teaching 
assistants,  teachers  and  Roma  parents  in  the 
education of Roma children” (OECD, 2016a, p. 8) 

institutions, 

Roma 

and 

50 

 
 
 
 
Annex 5. Background on PISA 

The OECD has been running this international large assessment of 15 year old students’ 
skills  in  Mathematics,  Science  and  Reading  every  three  years  since  2000.  Each  PISA 
assessment  cycle  has  a  major  domain,  which  in  2015  was  science.  The  fact  that  each 
cycle  targets  student  performance  in  a  particular  subject  means  that  domain-specific 
questions appear in the student, school and teacher questionnaires.  

PISA  assesses  students’  performance  in  science  through  questions  related  to  contexts, 
knowledge,  competencies  and  attitudes.  In  addition  to  cognitive  data,  PISA  gathers 
contextual  information  through  the  application  of  the  questionnaires15  and  this 
information  can  be  linked  to  student  performance  (OECD,  2016b,  p.  15).  More 
specifically, as defined by the OECD, the questionnaires cover the following aspects:   

—  “Aspects of students’ lives, such as their attitudes towards learning, their habits and 

life in and outside of school, and their family environment.” 

—  “Aspects of schools, such as the quality of the schools’ human and material resources, 
public  and  private  management  and  funding,  decision-making  processes,  staffing 
practices and the school’s curricular emphasis and extracurricular activities offered.” 

—  “Context  of  instruction,  including  institutional  structures  and  types,  class  size, 

classroom and school climate, and reading activities in class.” 

—  “Aspects of learning, including students’ interest, motivation and engagement.” 

Finally, in PISA, students’ test scores in Mathematics, Science and Reading are computed 
according  to  Item  Response  Theory  (IRT).  In  2015  the  mean  was  established  at  493, 
which  means  that  “approximately  two-thirds  of  all  students  in  OECD  countries  scored 
between  393  and  593  (i.e.,  within  one  standard  deviation  of  the  average)  on  this  PISA 
2015 assessment” (CMEC, 2016, p. 19). 

PISA 2015 counted with the participation of all 28 MS out of a total of 72 countries. The 
results show the differences and  similarities among education systems and how student 
outcomes are related to education practices and policies.  

15 Four additional were offered in PISA as optional: a computer familiarity questionnaire, an educational career 
questionnaire, a parent questionnaire, and a teacher questionnaire. 

51 

 
 
 
                                           
Annex 6. Missing data imputation  

PISA  2015  data  contain  information  for  all  students  about  their  science  performance. 
However,  additional  background  information  from  students  and  school  principals  have 
gaps due to missing responses. Data imputation techniques are necessary to address this 
issue  and  they  can  be  applied  as  the  amount  of  missing  data  per  variable  is  relatively 
small.  In  consequence,  sample  sizes  for  the  original  data  vary,  while  for  the  imputed 
dataset  there  is  no  missing  data  so  full  sample  size  is  available.  It  is  worth  noting  that 
while  there  is  substantial  variation  across  students  in  these  variables,  descriptive 
statistics  for  the  original  and  imputed  datasets  are  very  close.  In  our  case  we  used 
multiple  imputation  with  chained  equations  that  can  deal  with  different  data  types 
(continuous, ordered and nominal) (see Royston, 2009). 

52 

 
 
 
 
 
 
Annex 7. Details on the questions asked in PISA to construct the variables 

In the following we provide additional information on the variables included in this study, 
directly  taken  from  and  as  presented  by  OECD  sources  (2017a,  2017c),  except  for 
truancy which we constructed ourselves (see main text for more details). 

Expected school leaving 

ST111Q01TA 

Which of the following do you expect to complete? 

1 

2 

3 

4 

5 

6 

<ISCED level 2> 

<ISCED level 3B or C> 

<ISCED level 3A> 

<ISCED level 4> 

<ISCED level 5B> 

<ISCED level 5A or 6> 

Immigration background 
The  PISA  database  contains  three  country-specific  variables  relating  to  the  students’ 
country  of  birth,  their  mother  and  father  (COBN_S,  COBN_M,  and  COBN_F).  The  items 
ST019Q01TA, ST019Q01TB and ST019Q01TC were recoded into the following categories: 
(1)  country  of  birth  is  the  same  as  country  of  assessment  and  (2)  other.  The  index  of 
immigrant  background  (IMMIG)  was  calculated  from  these  variables  with  the  following 
categories:  native  students  (those  students  who  had  at  least  one  parent  born  in  the 
country),  (2)  second  generation  students  (those  born  in  the  country  of  assessment  but 
whose  parent(s)  were  born  in  another  country)  and  (3)  first-generation  students  (those 
students  born  outside  the  country  of  assessment  and  whose  parents  were  also  born  in 
another  country).  Students  with  missing  responses  for  either  the  student  or  for  both 
parents were assigned missing values for this variable. 

COBN_F 

COBN_M 

 COBN_S 

Country of Birth National Categories- Father 

Country of Birth National Categories- Mother 

Country of Birth National Categories- Self 

IMMIG 

Index Immigration status 

1 

2 

3 

Native 

Second-Generation 

First-Generation 

Truancy 
An index which we constructed using the responses to the test questions below: 

Item 

In the last two full weeks of school, how often did the following things occur? 

ST062Q01TA 

In the last two full weeks of school, how often: I <skipped> a whole school day 

ST062Q02TA 

In the last two full weeks of school, how often: I <skipped> some classes 

ST062Q03TA 

In the last two full weeks of school, how often: I arrived late for school 

53 

Epistemological beliefs 
Epistemological beliefs about science were measured with a new question about students’ 
views  on  scientific  approaches  (ST131).  Students  answered  on  a  four-point  Likert  scale 
with  the  answering  categories  “strongly  agree”,  “agree”,  “disagree”,  and  “strongly 
disagree”. The derived  variable EPIST was  scaled using the IRT scaling model described 
above.  [The  table  below]  shows  the  item  wording,  international  item  parameters  and 
item fit for EPIST. 

Item 
ST131Q01NA 
ST131Q03NA 
ST131Q04NA 
ST131Q06NA 
ST131Q08NA 
ST131Q11NA 

How much do you disagree or agree with the statements below? 
A good way to know if something is true is to do an experiment. 
Ideas in <broad science> sometimes change. 
Good answers are based on evidence from many different experiments. 
It is good to try experiments more than once to make sure of your findings. 
Sometimes <broad science> scientists change their minds about what is true in science. 
The ideas in <broad science> science books sometimes change. 

Grade repetition 
The  grade  repetition  variable  (REPEAT)  was  computed  by  recoding  variables 
ST127Q01TA,  ST127Q02TA,  and  ST127Q03TA.  REPEAT  took  the  value  of  “1”  if  the 
student  had  repeated  a  grade  in  at  least  one  ISCED  level  and  the  value  of  “0”  if  “no, 
never” was chosen at least once, given that none of the repeated grade categories were 
chosen. The index is assigned a missing value if none of the three categories were ticked 
in any levels. 

ST127Q01TA 

Have you ever repeated a <grade>? At <ISCED 1> 

ST127Q02TA 

Have you ever repeated a <grade>? At <ISCED 2> 

ST127Q03TA 

Have you ever repeated a <grade>? At <ISCED 3> 

Programme orientation 
Programme  orientation  (ISCEDO)  indicates  whether  the  programme’s  curricular  content 
was general, pre-vocational or vocational. Note: in Luxembourg also “modular” existing. 

ISCED level 
Programme  level  (ISCEDL)  indicates  whether  students  were  at  the  lower  or  upper 
secondary level (ISCED 2 or ISCED 3). 

Programme designation  
Programme designation (ISCEDD) indicates the designation of the study programme: (1) 
“A” (general programmes designed to give access to the next programme level); (2) “B” 
(programmes  designed  to  give  access  to  vocational  studies  at  the  next  programme 
level); (3) “C” (programmes designed to give direct access to the labour market); or (4) 
“M” (modular programmes that combine any or all of these characteristics). 

Sense of belonging 
PISA  2015  asked  students  about  their  sense  of  belonging  to  school  (ST034)  using  six 
trend items previously used in PISA 2012 (ID in 2012: ST87). The answering format was 
a  four-point  Likert  scale  with  the  answering  categories  “strongly  agree”,  “agree”, 
“disagree”,  and  “strongly  disagree”;  the  derived  IRT-Scale  is  named  BELONG.  Items 

54 

 
 
 
 
 
 
 
 
 
 
 
 
 
ST034Q02TA,  ST034Q03TA  and  ST034Q05TA  were  reverse-coded  so  that  higher  WLEs 
and higher difficulty correspond to higher level of sense of belonging on all items. 

Age at arrival 

ST021Q01TA  How old were you when you arrived in <country of test>? 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

age 0 - 1 

age 1 

age 2 

age 3 

age 4 

age 5 

age 6 

age 7 

age 8 

age 9 

age 10 

age 11 

age 12 

age 13 

age 14 

age 15 

age 16 

95 / .V 

97 / .N 

98 / .I 

99 / .M 

Valid Skip 

Not Applicable 

Invalid 

No Response 

SYSTEM MISSING  Missing 

Language at home 
Students indicated what language they usually speak at home (ST022), and the database 
includes  a  derived  variable  (LANGN)  containing  a  country-specific  code  for  each 
language.  In  addition,  an  internationally  comparable  variable  was  derived  from  this 
information  with  the  following  categories:  (1)  language  at  home  is  the  same  as  the 
language of assessment for that student and (2) language at home is another language. 

55 

Annex 8. Additional descriptive tables and figures  

PISA uses as criteria the age of students for inclusion in PISA, i.e. only 15 years olds are 
allowed to participate. As a consequence, some students are already in an ISCED3 year. 
While this does not mean that they may expect to complete ISCED3, this may influence 
the results. Therefore, one should control for this variable, as we do in our study. 

Table 6. Number of immigrants in PISA 2015 sample, ISCED 2 students only  

MS 

AT 

BE 

BG 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

Immigrants 

EU migrants 

Non-EU immigrants 

Total 

1st gen. 

2nd gen.  

Total 

1st gen. 

2nd gen. 

Total 

1st gen. 

2nd gen. 

38 

280 

6 

123 

944 

1673 

546 

60 

681 

229 

264 

4 

8 

480 

57 

220 

1588 

226 

2 

459 

10 

213 

19 

885 

23 

38 

27 

181 

4 

64 

212 

365 

35 

31 

556 

126 

142 

0 

6 

372 

51 

36 

673 

39 

2 

107 

6 

154 

7 

390 

16 

19 

11 

99 

2 

59 

732 

1308 

511 

29 

125 

103 

122 

4 

2 

108 

6 

184 

915 

187 

0 

352 

4 

59 

12 

495 

7 

19 

9 

72 

0 

29 

131 

2 

0 

 0 

 0 

38 

 0 

 0 

 0 

100 

10 

 0 

1229 

 0 

 0 

20 

 0 

31 

 0 

 0 

 0 

14 

9 

60 

0  

13 

41 

1 

 0 

 0 

 0 

33 

 0 

 0 

 0 

63 

9 

 0 

527 

 0 

 0 

18 

 0 

20 

 0 

 0 

 0 

10 

 0 

12 

0 

16 

90 

1 

 0 

 0 

 0 

5 

 0 

 0 

 0 

37 

1 

 0 

702 

 0 

 0 

2 

 0 

11 

 0 

 0 

 0 

4 

29 

208 

6 

94 

813 

1671 

546 

60 

681 

191 

264 

4 

8 

380 

47 

220 

359 

226 

2 

439 

10 

182 

19 

885 

23 

24 

18 

121 

4 

51 

171 

364 

35 

31 

556 

93 

142 

0 

6 

309 

42 

36 

146 

39 

2 

89 

6 

134 

7 

390 

16 

9 

11 

87 

2 

43 

642 

1307 

511 

29 

125 

98 

122 

4 

2 

71 

5 

184 

213 

187 

 0 

350 

4 

48 

12 

495 

7 

15 

1 
UK 
Note: orange = insufficient observations for EU migrants; green = sufficient observations for immigrants or EU 

 0 

6 

3 

8 

1 

6 

2 

9 

migrants. 

56 

 
 
 
 
 
 
An  important  aspect,  which  we  also  consider  in  our  regression  analysis,  is  the 
concentration of immigrants in schools. This concentration of immigrants has been seen 
in  the  literature  to  be  detrimental  to  learning  and  educational  outcomes,  as  the 
acquisition  of  language  skills  may  be  hampered,  but  most  importantly,  because  many 
migrants  students  come  from  a  lower  socio-economic  background.  The  OECD  has 
calculated an index of current concentration, which can be included alongside an index of 
maximum  potential  concentration  of 
immigrants.  This  current  concentration  of 
immigrants  varies  importantly  among  MS,  with  the  highest  shares  being  in  LU,  AT,  UK 
and BE (see Figure 23, concentration with at least 50 % immigrants).  

Figure 23. Index of current concentration of immigrants in schools 

%

20

18

16

14

12

10

8

6

4

2

0

y
m
o
n
o
c
e
/
y
r
t
n
u
o
c
e
h
t
n

i

s
t
n
e
d
u
t
s

7
1

K
U

2
5

U
L

0
2

T
A

8

4
1

8

T
I

E
I

I
S

8
1

E
B

7
1

E
D

7
1

E
S

1
1

K
D

1
1

R
H

1
1

L
E

1
1

S
E

3
1

R
F

7

T
P

1
1

L
N

0
1

E
E

t
n
a
r
g
m
m

i

i

f
o
e
g
a
t
n
e
c
r
e
P

Source: adapted from OECD (2016c). 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 24. Shares of immigrants 

n
w
o
d
k
a
e
r
b

t

n
e
c
r
e
p

100

80

60

40

20

0

T
A

E
B

G
B

Y
C

Z
C

E
D

K
D

E
E

L
E

I

S F
E

R
F

R
H

U
H

E

I

T

I

T
L

U
L

V
L

T
M

L
N

T
P

I

E S
S

K
S

K
U

Native

Migrant

Note:  Shares  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

As  concerns  expected  ESL,  distinguishing  additionally  between  first  and  second 
generation  immigrants  we  find  a  varied  picture  (not  shown).  Still,  the  tendency  is  that 
first generation immigrants have the highest probability to be expected ESL. In a number 
of  MS,  such as AT, BE, CZ,  DE, DK, EL,  ES, FR, SI, UK, there is an  upward trend when 
looking  at  natives,  second  generation  and  first  generation  immigrants.  In  other  words, 
the  lowest  numbers  of  expected  ESL  are  to  be  found  among  natives,  and  the  highest 
among the more recent first generation arrivals. This finding is intuitive, as more recent 
arrivals have in many cases to overcome language barriers and cultural differences. Still, 
the differences are often not very large with regard to second generation students, and in 
some  MS  the  expected  early  school  dropout  rates  are  even  lower  for  first  generation 
students than for second generation ones (such as IE, IT, LU, NL, SE). Interestingly, the 
only case where we find the opposite direction, with natives having the highest and first 
generation  students  the  lowest  expected  early  school  leaving  rates,  is  (again)  NL.  It 
appears that there is either a specific positive selection of immigrants during the recent 
decades, or that immigrants that go to NL have high expectations, which are even a bit 
higher  than  the  ones  of  locals.  In  addition,  first  generation  students  (but  not  second 
generation  immigrant  students)  have  also  lower  expected  dropout  rates  than  natives  in 
LU. 

58 

 
 
 
 
 
 
 
Figure 25. Shares of expected ISCED levels 

n
w
o
d
k
a
e
r
b
t
n
e
c
r
e
p

100

80

60

40

20

0

T
A

E
B

G
B

Y
C

Z
C

E
D

K
D

E
E

L
E

I

S F
E

R
F

R
H

U
H

E

I

T

I

T
L

U
L

V
L

L
N

T
P

I

E S
S

K
U

ISCED 2
ISCED 5-6

ISCED 3-4

Note:  Shares  not  calculated  for  MS  with  insufficient  number  of  observations  or  lacking  information  on 

educational expectations. 

59 

 
Figure 26. Resilient students in PISA 2015 

UK

SE

ES

SI

SK

PT

PL

NL

LU

LV

IT

IE

HU

EL

DE

FR

FI

EE

DK

CZ

BE

AT

immigrant background

natives

00

10

20

30

40

50

60

Source: based on OECD (2016c). 

Note:  the  OECD  definition  of  resilient  students  is  “[a]  student  is  classified  as  resilient  if  he  or  she  is  in  the 
bottom quarter of the PISA index of economic, social and cultural status (ESCS) in the country/economy of 
assessment and performs in  the top quarter of students among all countries/economies, after accounting 
for socio-economic status” 

60 

 
 
 
 
Annex 9. Multilevel regressions, country-by-country 

Here  we  consider  individual  MS,  pointing  out  the  relevance  of  other  variables  not 
mentioned  in  the  main  text.  We  only  comment  on  the  regression  models  including 
science  scores  and  significant  positive  or  negative  associations.  When  there  is  a 
significant  positive  association,  it  means  that  the  higher  this  factor  is,  the  higher  is 
expected  early  school  leaving  (ESL)  probability  or,  in  the  case  of  a  categorical  variable, 
the  expected  ESL  probability  is  significantly  higher  than  in  the  baseline  category.  The 
reverse is the case for negative associations. While in the ‘entire EU’ specification in the 
main  text  the  interaction  terms  were  always  insignificant,  they  are  relevant  only  in  a 
number of MS in this set of regressions. More specifically, we interacted immigrant status 
with various other explanatory variables. The resulting associations can be interpreted as 
follows:  for  example,  if  the  coefficient  on  the  interaction  between  immigrant  status  and 
sense  of  belonging  is  significantly  positive,  then  it  means  that  the  "effect"  of  sense  of 
belonging is stronger for immigrants than for natives. The detailed regression results can 
be found below. Given the fact that we have only expectations and not actual outcomes 
available (i.e., we cannot observe actual school leaving rates), it is possible that in some 
instances  rather  non-intuitive  results  may  appear.  For  example,  students  may  have 
unrealistically high expectations or ambitions in a specific school form in a MS. However, 
this  is  the  way  students  have  responded  to  the  PISA  questions  and  we  are  often  facing 
small  sample  sizes,  which  is  one  limitation  of  the  underlying  PISA  data.  In  most  cases, 
however, the findings are in line with intuitions.  

We  will  now  comment  on  each  MS  individually,  and  only  on  those  MS  where  there  are 
relevant  changes  to  what  has  been  said  in  the  main  text  (and  those  where  the  model 
could be calculated). Full regression results are provided below. 

—  AT:  being  a  student  in  programmes  designed  to  give  direct  access  to  the  labour 
market is positively associated, while having a pre-vocational programme orientation 
is negatively associated to expected ESL probability. 

—  BE: interestingly, we find the reverse case of AT, i.e., being a student in programmes 
designed  to  give  direct  access  to  the  labour  market  is  negatively  associated,  while 
having  a  pre-vocational  programme  orientation  is  positively  associated  to  expected 
ESL probability. 

—  BG:  the  school’s  concentration  of  immigrants  is  positively  related  to  expected  ESL 

probability. 

—  CY:  the  school’s  ESCS  and  the  school’s  concentration  of  immigrants  are  positively 

related to expected ESL probability. 

—  CZ:  Truancy,  and  being  in  programmes  designed  to  give  direct  access  to  the  labour 
market  are  positively  associated,  while  having  a  vocational  orientation  is  negatively 
associated to expected ESL probability. 

—  DE:  having  a  different  language  at  home  is  negatively  associated,  while  being  in 
programmes  designed  to  give  access  to  vocational  studies  at  the  next  programme 
level,  and  having  a  pre-vocational  orientation  are  positively  related  to  expected  ESL 
probability.  The  same  positive  significance  has  been  found  for  the  interaction  term 
between immigrant status and vocational orientation. 

—  DK: the school’s ESCS is positively related to expected ESL probability. 

—  EE:  the  time  of  arrival  is  negatively,  the  school’s  ESCS  is  positively  related  to 

expected ESL probability. 

—  EL:  speaking  a  different  language  at  home  is  negatively,  while  truancy  is  positively 
related  to  expected  ESL  probability.  Similarly,  the  interaction  term  of  immigrant 
status and language at home is positive. 

—  ES:  being  in  a  programme  designed  to  give  direct  access  to  the  labour  market  is 

negatively associated to expected ESL probability. 

61 

—  FI:  in  contrast  to  other  MS,  the  school’s  ESCS  is  negatively  related  to  expected  ESL 

probability. 

—  HR:  being  a  female  student  is  negatively  associated,  while  speaking  a  different 
language at home is positively associated to  expected  ESL probability. Similarly, the 
interaction  between  immigrant  status  and  language  at  home  is  positive,  while  the 
interaction between immigrant status and sense of belonging is negative.  

—  HU: age at arrival is negatively related, while being in a programme designed to give 
direct  access  to  the  labour  market  is  positively  associated  to  expected  ESL 
probability. In addition, the interaction with language at home is negative, while the 
interaction with senses of belonging and vocational programme orientation is positive. 

—  IT: Truancy is positively related to expected ESL probability. 

—  LT:  being  female  and  speaking  a  different  language  at  home  are  negatively 

associated to expected ESL probability. 

—  LU:  having  a  modular  programme  orientation  and  the  school’s  science  score  are 
positively  related  to  expected  ESL  probability,  as  is  also  the  interaction  between 
immigrant status and sense of belonging is positive.  

—  NL:  being  female  and  being  in  a  programme  designed  to  give  access  to  vocational 
studies  at  the  next  programme  level  as  well  as  the  school’s  ESCS  are  positively 
related to expected ESL probability. 

—  PT: speaking a different language at home and age of arrival are negatively related, 
while  truancy  and  being  in  vocational  orientation  and  a  school’s  concentration  of 
immigrants  are  positively  associated  to  expected  ESL  probability.  In  addition,  the 
interaction  of  immigrant  status  with  speaking  a  different  language  at  home  is 
positive, while it is negative its interaction with being in vocational orientation.  

—  SI:  being  in  a  programme  designed  to  give  access  to  vocational  studies  at  the  next 
programme  level  and  being  in  programmes  designed  to  give  direct  access  to  the 
labour market are positively related to expected ESL probability. The same is true for 
the concentration of immigrants in a school and a school’s science score. 

62 

Table 7. Multilevel regressions, country-by-country 

AT_logit

AT_

AT_pv BE_logit

BE_

BE_pv BG_logit

BG_

BG_pv CY_logit

CY_

CY_pv

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

(0.083)

-0.110
(0.236)
-0.189
(0.177)
0.112
(0.914)
0.105
(0.635)
-0.189
(0.812)
0.102
(0.077)

-0.121
(0.238)
0.160
(0.135)
0.044
(0.722)
-0.409
(0.379)
-0.691
(0.471)
-0.071
(0.064)
-0.319*
(0.148)

0.020
(0.183)
0.140
(0.111)
0.485
(0.589)
-0.494
(0.335)
-0.285
(0.362)
-0.101
(0.055)
-0.325*
(0.147)

0.178
(0.299)
-0.269
(0.269)
0.000
(.)
-3.870
(2.014)
-1.003
(0.836)
0.000
(.)
-0.207
(0.202)
-0.269
(0.166)
-0.107
(0.174)

-0.263
(0.260)
-0.208
(0.195)
0.000
(.)
-2.958
(2.104)
-0.882
(0.717)
0.000
(.)
-0.284*
(0.142)
-0.358*
(0.143)
-0.007
(0.133)

0.203
-0.407
-0.252
-0.241
(0.297)
(0.286)
(0.265)
(0.239)
-0.239
-0.282
-0.277
0.200
(0.257)
(0.208)
(0.211)
(0.138)
0.000
-0.559
-0.394
0.120
(.)
(1.640)
(1.547)
(0.729)
-4.505*
0.595
0.640
-0.483
(1.996)
(1.126)
(1.107)
(0.372)
-1.003
-0.931
-0.876
-0.766
(0.877)
(0.933)
(0.921)
(0.499)
0.000
0.129
0.137
-0.093
(.)
(0.093)
(0.103)
(0.067)
-0.186
-0.377*** -0.378*** -0.374***
-0.319*
(0.206)
(0.146)
(0.093)
(0.095)
-0.206
-0.450* -0.360*** -0.390*** -0.329**
-0.428*** -0.564**
(0.171)
(0.119)
(0.175)
(0.183)
(0.117)
-0.152
0.043
0.108
0.071
0.166
(0.303)
(0.348)
(0.172)
(0.132)
(0.158)
1.000* 1.017*** 1.097*** 1.002*** 2.169*** 2.719*** 2.589***
1.211**
1.143***
(0.695)
(0.276)
(0.399)
(0.406)
(0.285)
2.199*
-0.596
2.629
2.554
(1.883)
(0.307)
(1.757)
(0.887)
(2.310)
0.000
-0.269
-0.273
-0.140
(.)
(0.489)
(0.390)
(0.413)
-0.833* -0.975**
7.748** 10.682*** 10.626***
(0.375)
(0.370)
(2.030)
(2.154)
(2.736)
2.283**
2.237**
-8.072***-10.877***-10.562***
(0.726)
(0.683)
(1.772)
(1.715)
0.095
0.049
0.168
0.154
(0.215)
(0.204)
(0.348)
(0.329)

(0.261)
(0.198)
2.385 -0.753** -0.775**
(0.277)
(0.279)
0.000
(.)

-0.492
(0.324)
1.706**
(0.628)
-0.097
(0.200)

(0.089)
0.128
(0.104)

(0.111)
0.081
(0.129)

(2.277)
0.058
(0.296)

(0.669)
2.172*
(0.894)

(0.572)
2.301*
(0.957)

-0.323
(0.328)

-0.367
(0.304)

-0.418
(0.255)

0.009
(0.274)
0.031
(0.232)
0.115
(0.964)
-0.661
(0.883)
0.102
(0.552)
-0.034
(0.080)
-0.230
(0.199)
-0.263
(0.169)
0.276*
(0.116)
1.107*
(0.496)
-1.318
(0.703)

0.028
(0.274)
0.007
(0.237)
0.011
(0.993)
-0.678
(0.874)
0.080
(0.561)
-0.028
(0.082)
-0.232
(0.197)
-0.258
(0.168)
0.277*
(0.116)
1.137*
(0.497)
-1.338
(0.732)
1.705*** 1.718***
(0.483)

(0.477)

-0.050
(0.269)
0.047
(0.216)
0.156
(0.853)
-0.748
(0.889)
0.106
(0.572)
-0.032
(0.070)
-0.197
(0.188)
-0.060
(0.177)
0.204*
(0.104)
0.872
(0.493)
-0.825
(0.934)
0.942
(0.730)

0.000
(.)

0.000
(.)

0.000
(.)

(.)

-1.840*
(0.723)

0.000 -1.934**
(0.713)

-0.412
(0.993)
0.196
(0.178)
0.509
(0.598)
-0.376
(0.686)

-0.258
(1.349)
-0.086
(0.368)
0.616
(0.529)
-0.249
(1.113)

-0.507
(1.491)
-0.149
(0.411)
0.495
(0.566)
-0.002
(1.204)

0.460
(0.567)
-0.365
(0.232)
0.665*
(0.295)
0.442
(0.590)

0.436
(0.584)
-0.391
(0.231)
0.081
(0.407)
0.676
(0.605)

0.000
(.)
3.695*
(1.502)
-0.341
(0.260)
3.880
(6.164)

0.000
(.)
4.898**
(1.612)
-0.090
(0.247)
8.662
(5.395)

-0.137
(0.524)
-0.376
(0.227)
0.641**
(0.229)
0.064
(0.508)

-1.847*
(0.759)
4.674** -1.380*** -1.399*** -1.324***
(0.364)
(1.618)
1.030
-0.197
(0.564)
(0.280)
1.836*
9.904
(0.848)
(5.277)
24.362*** 25.435*** 25.618*** 18.021*** 15.612*** 17.428*** 11.767*** 10.990*** 11.523*** 9.594*** 9.607*** 8.387***
(1.626)
-0.015*
(0.008)
-0.008***
(0.002)
1.007
(2.797)

(3.070)
-0.000
(0.004)
-0.007**
(0.003)
-7.405* -4.374*** -4.319*** -6.738*** -7.037*** -6.957*** -7.499*** -4.856*** -4.844***
(0.682)
(0.982)
(2.921)

(2.301)
0.005
(0.004)
-0.005**
(0.002)

-7.805*** -7.949***
(1.665)

(0.343)
0.436
(0.405)
1.191
(0.802)

(1.858)
0.001
(0.003)
-0.005
(0.003)

(0.341)
0.421
(0.399)
1.234
(0.774)

(1.740)

(1.598)

(0.317)

(0.955)

(1.606)

(2.989)

(3.601)

(2.193)

(1.825)

(2.087)

(1.529)

(0.361)

(1.683)

(0.662)

(1.924)

63 

 
 
 
Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

CZ_logit

CZ_

CZ_pv DE_logit

DE_

DE_pv DK_logit

DK_

DK_pv

EE_logit

EE_

EE_pv

(0.160)

(0.413)

-1.384
(0.748)

0.083
(0.230)

0.221
(0.153)

-0.160
(0.195)

0.074
(0.085)

-0.217
(0.118)

-1.713*
(0.796)

-1.730*
(0.858)

(0.053)
0.010
(0.361)
0.092
(0.148)
-0.003
(0.313)
0.013
(0.038)

(0.109)
0.329
(0.541)
0.214
(0.223)
-0.425
(0.438)
0.003
(0.052)

-0.724* -0.866**
0.131 -0.738***
(0.314)
(0.299)
(0.192)
-0.381
-0.558*
-1.469*** -1.360** -1.177** -0.545*** -0.569*** -0.539*** -0.421*** -0.515*** -0.412*** -0.536***
(0.268)
(0.247)
(0.145)
(0.120)
(0.094)
(0.094)
(0.067)
(0.454)
(0.401)
0.324
0.636
0.187
0.062
-0.006
0.183
-0.385
-12.387* -12.227* -14.889**
(0.697)
(0.828)
(0.753)
(0.513)
(0.525)
(0.569)
(0.533)
(5.158)
(5.369)
0.340
0.568
0.372
0.079
-0.630* -0.866**
-0.443
-3.444
-2.470
(0.389)
(0.422)
(0.279)
(0.250)
(0.286)
(0.302)
(0.260)
(3.563)
(3.212)
-0.427
-0.035
-0.011
-0.507
-0.757*
-0.098
-0.149
1.126
0.676
(0.889)
(0.839)
(0.489)
(0.418)
(0.316)
(0.309)
(0.317)
(1.013)
(1.049)
-0.089
-0.105
-0.102
0.017
-0.081
-0.062
-0.003
0.806**
0.685*
(0.057)
(0.092)
(0.070)
(0.073)
(0.074)
(0.050)
(0.066)
(0.264)
(0.281)
-0.280* -0.564** -0.539**
-0.076 -0.152*** -0.166** -0.173**
-0.042
-0.055
-0.121
-0.268
(0.190)
(0.183)
(0.053)
(0.073)
(0.137)
(0.077)
(0.424)
(0.479)
(0.063)
-0.082
-0.345
-0.072 -0.353**
-0.154*
-0.724 -0.180***
-0.701
(0.219)
(0.203)
(0.111)
(0.074)
(0.067)
(0.053)
(0.381)
(0.381)
0.024
0.193
0.206**
0.095
-0.006
0.051
0.666*
0.723*
(0.110)
(0.101)
(0.074)
(0.056)
(0.307)
(0.330)
(0.094)
(0.077)
0.618
1.153*
0.918*
-0.205
1.934* 0.724*** 0.697***
2.111*
(0.578)
(0.547)
(0.358)
(0.345)
(0.857)
(0.178)
(0.147)
(0.812)
0.000
0.000
0.000
0.000
1.301
(.)
(.)
(.)
(.)
(1.368)
0.000
0.000
0.000
(.)
(.)
(.)

(0.064)
-0.110
(0.097)
0.409*
(0.184)
0.178-14.133***-16.036***-16.810***
(0.373)
16.524
(.)

(0.051)
(0.041)
0.008 -0.213*** -0.219***
(0.064)
(0.039)
0.159**
0.112**
(0.050)
(0.038)
0.148
0.306
(0.338)
(0.224)
0.000
0.000
(.)
(.)

(5.581)
-3.238
(3.341)
1.507
(0.901)
0.584*
(0.268)
-0.492
(0.400)
-0.454
(0.322)
0.407
(0.306)
1.727*
(0.744)
0.935
(1.021)

(0.375)
13.997***
(0.534)

(0.373)
15.900
(.)

(0.995)

1.925**
(0.725)

1.257
(0.842)

2.359*
(0.941)

-2.118*
(0.869)

-2.015
(1.054)

-0.858
(1.015)

0.918*** 1.215*** 1.844***
(0.374)
(0.242)
0.000
0.000
(.)
(.)

(0.199)
0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.489
(1.839)
-3.338
(1.753)
0.047
(0.702)
1.532
(3.383)

-0.166
(0.454)
0.193
(0.192)
-0.207
(0.222)
0.479
(0.321)

0.283
(0.446)
0.188
(0.191)
0.047
(0.240)
0.767*
(0.377)

-0.076
(0.371)
0.045
(0.113)
0.131*
(0.056)
-0.150
(0.165)

0.162
(0.541)
-0.161
(0.174)
0.116
(0.071)
-0.423
(0.286)

0.646
(1.781)
-3.806*
(1.706)
0.059
(0.582)
-1.375
(4.941)

0.262
(1.668)
-3.869*
(1.902)
-1.018
(0.989)
4.888
(3.781)

-0.239
(0.446)
0.183
(0.152)
-0.386*
(0.190)
0.254
(0.297)

-0.539
(1.181)
0.522
(0.318)
0.869*
(0.342)
0.639
(0.769)
43.457*** 44.020*** 52.234*** 5.671*** 5.561*** 5.757*** 5.981*** 6.192*** 6.580*** 14.179*** 14.391*** 16.135***
(1.875)
(0.343)
(0.450)
-0.000
-0.000
-0.005**
(0.003)
(0.001)
(0.002)
-0.012***
-0.007***
-0.010***
(0.002)
(0.002)
(0.001)
-0.534 -2.830*** -2.931*** -2.970*** -4.177*** -4.348*** -4.438**
(1.619)
(0.687)
(1.078)

-7.892*** -8.448*** -17.772** -3.067*** -3.240***
(0.262)

-0.297
(0.776)
0.264
(0.263)
0.588**
(0.213)
-0.011
(0.611)

-0.623
(1.076)
0.616*
(0.302)
0.860**
(0.268)
-0.056
(0.698)

0.154
(0.507)
-0.149
(0.173)
0.086
(0.093)
-0.208
(0.300)

(8.425)
0.018
(0.009)
-0.009
(0.009)

(0.170)

(0.290)

(0.209)

(1.544)

(0.382)

(0.154)

(4.471)

(0.761)

(0.997)

(0.233)

(1.583)

(4.748)

(0.093)

(0.143)

(5.734)

(0.341)

64 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

EL_logit

EL_

EL_pv

ES_logit

ES_

ES_pv

FI_logit

FI_

FI_pv

FR_logit

FR_

FR_pv

-0.041
(0.113)

-0.191
(0.125)

-0.103
(0.082)

-0.191
(0.111)

-0.090
(0.101)

-0.232*
(0.106)

(0.067)
0.467
(0.715)
-0.422
(0.512)
-0.357
(0.304)
-0.103
(0.066)

(0.079)
1.301
(0.854)
-0.272
(0.516)
0.030
(0.445)
-0.173*
(0.080)

(0.077)
(0.075)
0.530
-0.041
(0.701)
(0.435)
-0.830
-0.504
(0.499)
(0.453)
-0.036
0.246
(0.470)
(0.142)
-0.145*
-0.041
(0.048)
(0.073)
-0.095 -0.140** -0.169** -0.180**
(0.056)
(0.048)
(0.055)
(0.048)
-0.032
-0.053 -0.175*** -0.155**
(0.049)
(0.047)
(0.047)
(0.076)
0.021
0.070
0.062
0.043
(0.045)
(0.046)
(0.037)
(0.063)
0.243
0.614*
0.478*
1.092 1.674*** 1.733*** 1.156***
(0.276)
(0.295)
(0.216)
(0.127)
0.000
0.000
0.000
0.000
(.)
(.)
(.)
(.)

0.094
-0.728
-0.688
-0.425
(0.632)
(0.124)
(0.642)
(0.491)
0.983* -0.418*** -0.398*** -0.339*** -0.441*** -0.364*** -0.253** -0.276**
0.920
0.505
(0.077)
(0.052)
(0.086)
(0.437)
(0.472)
(0.372)
0.068
-0.308
-0.213
-0.745
-1.091
-2.663
(0.453)
(0.359)
(0.690)
(2.323)
(2.302)
(2.011)
-0.251
0.053
0.624**
0.797
0.856
0.567
(0.463)
(0.356)
(0.237)
(0.677)
(0.678)
(0.697)
0.296*
0.298*
0.600*
1.483
1.441
1.606
(0.150)
(0.135)
(0.298)
(1.105)
(1.140)
(0.981)
-0.024
-0.001
0.101
-0.046
0.011
0.202
(0.049)
(0.043)
(0.072)
(0.129)
(0.212)
(0.228)
0.003
-0.091
-0.084
-0.579* -0.747** -0.725**
(0.092)
(0.249)
(0.267)
(0.268)
(0.049)
(0.045)
-0.101
-0.218 -0.218*** -0.232**
-0.346
-0.041
(0.062)
(0.080)
(0.052)
(0.240)
(0.259)
(0.186)
-0.003
0.084
0.101
-0.395
-0.372
0.023
(0.064)
(0.327)
(0.262)
(0.062)
(0.054)
(0.330)
1.067**
1.512
1.288
(0.328)
(0.736)
(0.793)
-0.913*
-0.456
-0.278
(0.434)
(1.006)
(0.995)
0.651*
(0.296)
0.270
(0.605)
-0.659
(0.353)
0.000
(.)

-1.165* -1.284**
(0.497)
(0.456)
0.000
0.000
(.)
(.)

-0.883
(0.473)
0.000
(.)

(0.113)
0.000
(.)

(0.133)
0.000
(.)

(0.871)
-0.016
(1.032)

-1.173
(0.940)

-0.665
(0.805)

0.278
(0.708)

0.000
(.)

0.000
(.)

0.000
(.)

0.143
(0.139)
-0.149
(0.095)
-0.600
(0.724)
0.488
(0.291)
0.316
(0.333)
0.119
(0.066)
0.085
(0.106)
-0.091
(0.066)
-0.056
(0.069)
0.924**
(0.343)
-0.756
(0.420)
0.430
(0.370)
0.317
(0.548)
-0.538
(0.413)
0.000
(.)

0.083
(0.139)
-0.108
(0.095)
-0.610
(0.779)
0.417
(0.310)
0.183
(0.369)
0.113
(0.070)
0.090
(0.102)
-0.060
(0.065)
-0.099
(0.069)
0.943**
(0.337)
-0.492
(0.463)
0.254
(0.395)
0.021
(0.552)
-0.576
(0.433)
0.000
(.)

-1.282
(1.290)
-0.211
(0.435)
-0.271
(0.489)
-0.360
(1.534)

-1.295
(1.177)
0.453
(0.495)
0.079
(0.468)
0.326
(1.865)

-1.266
(1.297)
0.382
(0.484)
0.223
(0.509)
0.094
(1.505)

-0.593
(0.359)
-0.064
(0.134)
0.025
(0.094)
0.473
(0.292)

0.448
(0.631)
0.259
(0.172)
-0.013
(0.092)
0.046
(0.349)

-0.228
(0.862)
0.293
(0.174)
-0.139
(0.120)
0.035
(0.415)

-0.697*
(0.324)
-0.035
(0.138)
0.125
(0.082)
0.108
(0.315)

-0.591*
(0.272)
0.034
(0.121)
0.092
(0.074)
0.091
(0.277)

-0.526
(0.557)
-0.060
(0.232)
0.010
(0.185)
-0.198
(0.266)
9.565*** 8.662*** 8.692*** 7.708*** 7.715*** 8.507*** 7.469*** 7.061*** 7.299*** 4.542*** 4.722*** 4.844***
(0.348)
-0.003
(0.002)
-0.004***
(0.001)
-2.095*
(0.874)

(1.391)
-0.008
(0.007)
-0.007
(0.005)
-1.798 -3.919*** -3.954*** -4.112*** -3.008*** -2.867*** -3.353*** -3.137*** -3.263***
(0.405)
(0.114)
(3.034)

0.023 -1.271**
(0.458)
-0.077
(0.187)
0.152
(0.180)
-0.330
(0.287)

-0.636
(0.518)
-0.047
(0.225)
-0.086
(0.150)
-0.281
(0.265)

(0.842)
0.360*
(0.165)
-0.238
(0.134)
0.475
(0.410)

(0.532)
0.001
(0.001)
-0.005***
(0.001)

(0.480)
0.000
(0.001)
-0.010***
(0.001)

-5.496*** -4.931***
(1.058)

(0.523)

(0.370)

(0.117)

(0.141)

(0.552)

(1.382)

(2.055)

(1.017)

(0.468)

(0.594)

(0.413)

(0.435)

(0.123)

(0.506)

(0.776)

65 

 
  
Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

HR_logit

HR_

HR_pv HU_logit

HU_

HU_pv

IT_logit

IT_

IT_pv

-0.971
(1.168)
0.667
(0.512)
0.000
(.)

-1.612
(1.046)
0.575
(0.523)
0.000
(.)

-1.801
(1.085)
0.628
(0.522)
0.000
(.)
-12.997***-15.862***-14.874***
(0.883)
2.355*
(0.925)

(0.527)
1.988**
(0.623)
0.000
(.)
-0.461
(0.345)
-0.808
(0.645)
0.327
(0.329)
2.531
(1.399)
0.000
(.)

(1.082)
2.082**
(0.687)
0.000
(.)
-0.420
(0.364)
-0.955
(0.656)
0.267
(0.300)
2.704
(1.451)
0.000
(.)

0.236
0.015
(0.271)
(0.191)
-0.160
-0.191
(0.151)
(0.236)
8.109* 11.190***
(3.216)
(3.546)
0.133
0.270
(0.727)
(0.643)
0.548
0.166
(0.567)
(0.427)

0.192
(0.278)
-0.113
(0.245)
9.772**
(3.114)
0.196
(0.727)
-0.008
(0.582)
0.000 -7.669*** -9.962*** -8.659***
(1.834)
0.099
(0.163)

(.)
-0.516
(0.448)
-0.764
(0.701)
0.180
(0.331)
2.394
(1.553)
0.000
(.)

(1.981)
-0.129
(0.111)
-0.046
(0.148)
0.230*
(0.099)
0.001
(0.368)
-0.853*
(0.388)

(1.864)
0.077
(0.159)
-0.014
(0.204)
0.133
(0.145)
0.002
(0.448)
-0.989*
(0.435)

-0.109
-0.086
(0.429)
(0.268)
-0.369
-0.332
(0.171)
(0.273)
-1.593 -4.819**
(1.765)
(1.420)
-2.018*
0.251
(0.937)
(0.596)
0.137
0.366
(0.614)
(0.384)
0.280*
0.170
(0.121)
(0.115)
0.078
-0.223
(0.356)
(0.197)
-0.404
0.015 -0.458**
(0.215)
(0.149)
0.380*
0.335**
(0.163)
(0.109)
1.432**
0.881**
(0.527)
(0.270)
2.475
2.993**
(1.456)
(1.072)

(0.199)
0.101
(0.143)
-0.161
(0.484)
-1.094*
(0.435)

0.006
(0.416)
-0.341
(0.288)
-4.833*
(1.965)
-1.881*
(0.960)
0.160
(0.608)
0.282*
(0.137)
0.094
(0.344)
-0.411*
(0.194)
0.384*
(0.161)
1.350*
(0.589)
1.980
(1.399)

0.247
(0.739)

0.110
(0.655)

-0.422 1.224*** 1.442*** 1.492***
(0.382)
(1.047)

(0.316)

(0.352)

-0.850
(0.537)

-0.240
(0.532)

-0.236
(0.528)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.158
(0.306)

-0.119
(0.541)

0.231
(0.496)

(.)
-1.431
(0.825)
-1.896
(1.890)
-3.693
(5.599)

1.901
(1.173)
-0.401
(0.324)
0.393
(0.208)
2.019
(1.366)

0.458
(1.101)
0.079
(0.266)
0.038
(0.192)
1.619
(1.710)

1.735
(1.167)
-0.387
(0.340)
0.400
(0.215)
1.937
(1.456)

-0.139
(0.645)
-0.684*
(0.282)
0.580*
(0.272)
1.489
(0.889)

1.755
(0.971)
-1.059*
(0.425)
0.419
(0.404)
1.646
(1.181)

15.546***
(1.133)
-1.002
(0.746)
-1.278
(1.729)
-4.363
(4.637)

18.657 17.966***
(1.148)
-1.311
(0.897)
-1.185
(2.021)
-5.049
(5.341)

1.420
(1.011)
-1.238*
(0.488)
0.165
(0.432)
1.587
(1.121)
90.747***100.132***108.761*** 7.741*** 8.193*** 8.455*** 19.625*** 16.603*** 17.895***
(2.923)
(0.751)
(13.729)
0.006
0.000
-0.019
(0.004)
(0.003)
(0.016)
-0.005*
-0.006**
-0.009
(0.009)
(0.002)
(0.002)
-0.969 -3.736*** -3.652*** -3.622** -8.750*** -8.088***-10.567***
(2.539)
(1.268)
(7.752)

-8.903*** -9.695***
(1.384)

(15.331)

(17.458)

(0.657)

(0.673)

(0.360)

(0.386)

(1.209)

(1.684)

(2.381)

(2.858)

(1.119)

Note: The model for IE did not converge, so that IE is not shown here. 

66 

 
LT_logit

LT_

LT_pv

LU_logit

LU_

LU_pv

LV_logit

LV_

LV_pv NL_logit

NL_

NL_pv

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

-0.474
(0.295)

0.238
(0.152)

0.238
(0.152)

0.172 -0.583**
(0.204)

(0.076)
-1.478*
(0.627)
-0.771
(0.422)
-0.177
(0.375)
0.026
(0.040)

(0.076)
-1.478*
(0.627)
-0.771
(0.422)
-0.177
(0.375)
0.026
(0.040)

(0.242)
-0.542***
(0.135)
0.000
(.)
-0.307
(0.566)
-0.323
(0.411)
0.000
(.)
-0.168
(0.088)

(0.092)
-0.379*** -0.451*** -0.364** -0.180** -0.180**
(0.063)

(0.120)
1.771
(1.148)
0.807*
(0.386)
-0.017
(0.309)
0.026 -0.244**
(0.075)

0.262** 0.378*** 0.382***
-0.377
-0.725** -0.741** -0.838**
(0.101)
(0.101)
(0.092)
(0.300)
(0.274)
(0.262)
(0.150)
-0.168*
-0.167*
-0.179*
-0.281 -0.265*** -0.265*** -0.250*** -0.567*** -0.644*** -0.541**
-0.370
(0.084)
(0.085)
(0.078)
(0.177)
(0.076)
(0.205)
(0.193)
(0.167)
-2.100
-2.110
-2.132*
0.213
-1.531*
0.000
0.000
0.972
(1.103)
(1.097)
(0.845)
(1.222)
(0.622)
(.)
(.)
(1.353)
-0.476
-0.484
-0.376
0.584
-0.832
-0.462
-0.652
0.956*
(0.285)
(0.284)
(0.260)
(0.443)
(0.439)
(0.606)
(0.614)
(0.447)
-0.595
-0.595
-0.459
-0.127
-0.221
-0.983
-1.078
-0.010
(0.397)
(0.398)
(0.376)
(0.327)
(0.372)
(0.596)
(0.612)
(0.372)
0.199
0.199
0.221*
-0.196* -0.165**
0.000
0.000
(0.114)
(0.114)
(0.093)
(0.061)
(0.081)
(.)
(.)
(0.041)
-0.052
-0.053
-0.041
-0.107 -0.347*** -0.347*** -0.354*** -0.317** -0.405** -0.359**
-0.114
(0.053)
(0.052)
(0.056)
(0.138)
(0.093)
(0.113)
(0.109)
-0.088
-0.084
-0.054
0.013
-0.126*
(0.074)
(0.068)
(0.064)
(0.128)
(0.064)
0.059
0.057
0.032
0.171
0.185 0.211*** 0.211*** 0.197***
(0.073)
(0.072)
(0.068)
(0.121)
(0.056)
0.108
0.102
0.125
0.789
(0.421)
(0.142)
(0.142)
(0.131)
-0.763 -3.083*** -3.400*** -3.404***
(0.735)
(0.657)
0.000 1.204*** 1.252*** 1.238***
(0.307)
0.308
(1.008)
0.189
(0.155)
0.000
(.)

(0.131)
0.000 -1.584*** -1.584*** -1.470***
(0.310)
0.053
(0.567)
-0.450
(0.923)

(0.103)
-0.167
(0.106)
0.176*
(0.088)
0.220 1.091***
(0.331)
-0.826
(0.710)
0.000
(.)

(0.125)
0.278*
(0.108)
1.603*** 1.703***
(0.409)
0.000
(.)

(0.140)
-0.144
(0.127)
0.219
(0.116)
1.276**
(0.426)
-0.972
(0.726)
0.000
(.)

(0.283)
0.314
(0.980)
0.186
(0.133)
0.000
(.)

(0.245)
0.223
(0.966)
0.129
(0.103)
0.000
(.)

(0.290)
0.120
(0.565)
-0.358
(0.891)

(0.290)
0.120
(0.565)
-0.358
(0.891)

(0.102)
0.271***
(0.077)

(0.117)
1.201**
(0.398)

(0.057)
0.291*
(0.132)

(0.370)
0.000
(.)

(0.057)
0.291*
(0.132)

0.291
(0.381)

-0.120
(0.703)

0.088
(0.587)

0.299
(0.390)

0.000
(.)

0.000
(.)

0.000
(.)

(0.092)

(0.063)

(0.734)

(0.134)

(0.683)

(.)

(.)

0.291
(0.381)
0.941*** 0.941***
(0.258)
0.631
(0.466)
0.267**
(0.102)
0.600**
(0.187)
0.667*
(0.336)

(0.258)
0.631
(0.466)
0.267**
(0.102)
0.600**
(0.187)
0.667*
(0.336)

0.329
(0.396)
0.723**
(0.255)
0.574
(0.474)
0.286**
(0.103)
-0.047
(0.241)
0.795*
(0.315)

-0.354
(1.190)
-0.327
(0.289)
0.064
(0.219)
1.089
(0.714)

0.455
(1.258)
-0.411
(0.354)
0.309
(0.208)
-0.265
(1.418)

0.229
(1.239)
-0.535
(0.367)
0.262
(0.293)
-0.505
(1.452)

-0.415
(0.949)
0.317
(0.436)
0.307
(0.159)
-0.269
(0.941)

-0.732
(1.099)
0.356
(0.393)
0.364
(0.186)
-0.379
(1.204)

-0.160
(0.582)
0.328
(0.167)
0.281*
(0.126)
0.605*
(0.283)
17.604*** 18.154*** 18.084*** 9.201*** 9.201*** 11.571*** 18.660*** 17.451*** 19.267*** 5.866*** 5.748*** 5.735***
(0.291)
-0.000
(0.001)
0.000
(0.001)
-4.815*** -4.699*** -4.234** -3.379*** -3.379*** -6.669*** -4.504*** -4.613*** -6.688*** -4.057*** -4.158*** -4.082***
(0.751)

-0.157
(0.583)
0.328
(0.167)
0.265
(0.149)
0.614*
(0.278)

-0.313
(1.070)
0.368
(0.362)
0.103
(0.263)
0.175
(1.139)

-0.163
(0.516)
0.283
(0.157)
0.183
(0.129)
0.542
(0.281)

(2.078)
0.003
(0.003)
-0.015***
(0.002)

(1.739)
-0.001
(0.003)
-0.010***
(0.002)

(1.274)
0.007**
(0.002)
-0.004***
(0.001)

(0.422)

(1.103)

(0.299)

(0.276)

(0.257)

(1.786)

(1.190)

(1.792)

(0.171)

(0.239)

(0.273)

(0.193)

(0.180)

(1.598)

(1.643)

(1.463)

(0.422)

(1.791)

(1.103)

67 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

PT_logit

PT_

PT_pv

SE_logit

SE_

SE_pv

SI_logit

SI_

SI_pv

-0.043
(0.388)
-0.197
(0.333)
-2.270
(1.727)
0.646
(1.090)
0.411
(1.002)
0.162
(0.127)

-0.087
(0.511)
-0.578
(0.519)
-1.143
(1.875)
0.628
(1.268)
0.442
(1.553)
0.049
(0.177)

-0.367*
-0.126
-0.413* -0.438**
-0.169
-0.189
0.086
(0.144)
(0.260)
(0.170)
(0.299)
(0.168)
(0.136)
(0.508)
-0.533***
-0.468*
-0.233*
-0.413* -0.296** -0.327**
-0.458
(0.104)
(0.197)
(0.108)
(0.109)
(0.092)
(0.200)
(0.540)
0.443
0.974
-0.067
-0.099
-0.016
1.068
-1.578
(0.568)
(0.754)
(0.619)
(0.678)
(0.678)
(0.738)
(1.897)
0.204
0.395
-0.057
0.593
0.512
0.417
0.607
(0.396)
(0.382)
(0.334)
(0.657)
(0.597)
(0.478)
(1.252)
0.531
0.645*
0.759**
-1.456* -2.883*** -2.855***
1.060
(0.317)
(0.305)
(0.268)
(0.762)
(0.735)
(0.594)
(1.682)
0.007
0.040
-0.279
-0.289
-0.164*
0.051
0.107
(0.187)
(0.072)
(0.083)
(0.071)
(0.149)
(0.157)
(0.082)
-0.020 -0.608*** -1.046*** -1.023***
-0.042
-0.111
-0.004
-0.044
-0.091
(0.311)
(0.070)
(0.071)
(0.070)
(0.078)
(0.084)
(0.057)
-0.118 -0.237*** -0.310*** -0.202**
-0.156
-0.156*
-0.086
(0.332)
(0.070)
(0.084)
(0.082)
(0.073)
-0.120
0.161
0.196*
0.177**
-0.152
(0.355)
(0.110)
(0.103)
(0.097)
(0.064)
2.284**
-0.145
0.215
0.370
0.240
(0.870)
(0.503)
(0.605)
(0.564)
(0.360)
0.000 -2.510** -3.034*** -4.156***
-4.208*** -3.882*** -4.046***
(0.862)
(0.991)
(1.079)
3.495**
1.920**
1.741**
(1.117)
(0.587)
(0.636)
0.000 2.894*** 3.697*** 5.844***
0.000
0.000
(.)
(1.658)
(.)
-0.402
-0.197
(0.416)
(0.407)
3.179*** 2.967*** 2.952***
(0.558)
(0.582)

(0.832)
1.918***
(0.566)
0.000
(.)
-0.218
(0.306)

(0.171)
-0.129
(0.231)
0.091
(0.192)
1.649**
(0.623)

(0.063)
-0.051
(0.108)
-0.019
(0.524)
0.000
(.)

(0.052)
0.079
(0.081)
-0.057
(0.341)
0.000
(.)

(0.295)
-0.010
(0.341)
-0.115
(0.347)
1.647*
(0.696)

(0.711)
2.438**
(0.877)

(0.788)
1.854*
(0.742)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

0.000
(.)

(0.560)

(1.065)

(0.853)

(.)

(.)

(1.062)
0.128
(0.458)
-0.029
(0.196)
0.149
(0.477)

(1.062)
0.063
(0.488)
-0.330
(0.207)
0.407
(0.471)

-0.926
(0.520)
-0.087
(0.186)
-0.294
(0.215)
0.957*
(0.395)

-0.431
(1.790)
-0.381
(0.598)
2.590**
(0.903)
1.639
(2.070)

-0.077
(1.508)
-0.321
(0.409)
1.717**
(0.567)
0.103
(1.491)

2.452** 3.781*** 3.617*** -1.249**
(0.447)
(0.855)
0.173
0.346
(0.153)
(0.383)
-0.131
0.177
(0.144)
(0.142)
0.149
-0.521
(0.355)
(0.492)

-1.033
-1.115*
(1.966)
(0.492)
-0.404
-0.051
(0.585)
(0.187)
2.232**
0.007
(0.822)
(0.178)
3.178
0.449
(2.746)
(0.387)
8.315*** 7.639*** 8.498*** 9.952*** 8.943*** 9.534*** 11.433*** 13.203*** 14.248***
(2.841)
(0.941)
0.016*
(0.007)
0.001
(0.006)
-2.799*** -3.341*** -5.479** -3.478*** -3.398*** -5.052*** -5.177*** -5.681***-13.906***
(4.130)

(1.005)
0.003
(0.002)
-0.006***
(0.001)

(1.256)
0.004
(0.003)
-0.005*
(0.002)

(0.955)

(0.141)

(0.162)

(0.923)

(0.999)

(0.355)

(1.743)

(0.539)

(2.453)

(0.842)

(1.537)

(0.872)

(0.622)

Note: The model for the UK did not converge, so that it is not shown here. 

68 

 
Annex 10. Multilevel regressions, distinction among EU and non-EU migrants 

A.  Comparison of EU and non-EU immigrants to natives 

These regressions compare separately EU and non-EU migrants to natives. The baseline 
category are natives. 

The  column  prefix  “eu”  means  that  EU  migrant  students  are  considered,  while  “neu” 
means  that  non-EU  migrant  students  are  considered.  As  in  the  other  multilevel 
regressions,  the  comparison  group  are  native  students.  The  identical  strategy  as  in  the 
other regressions is used. Columns (1) and (2) indicate regressions including all available 
MS, as in the main text. 

69 

1. All MS

70 

Table 8. Multilevel regressions, all MS, for EU migrants 

(1)

(2)

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

0.067
(0.106)
-0.435***
(0.052)
-1.473
(0.985)
-0.663
(0.631)
0.149
(0.128)
0.123
(0.116)
-0.031
(0.043)
-0.145**
(0.044)
0.055
(0.046)
1.053***
(0.115)
-0.541**
(0.178)
0.262
(0.247)
0.465
(0.368)
-0.322
(0.253)
0.176
(0.233)
1.021***
(0.261)
-1.13
(0.727)
-0.611
(0.386)
-0.129
(0.067)
2.792***
(0.843)
5.885***
(0.2)

-4.137***
(0.328)

-0.069
(0.093)
-0.364***
(0.052)
-1.473
(1.002)
-0.543
(0.578)
-0.009
(0.151)
0.11
(0.113)
-0.039
(0.042)
-0.035
(0.041)
-0.013
(0.047)
0.752***
(0.115)
-0.534**
(0.192)
0.144
(0.271)
0.316
(0.364)
-0.568*
(0.285)
0.089
(0.24)
0.538*
(0.257)
-1.383
(0.745)
-0.916*
(0.376)
-0.066
(0.079)
3.071***
(0.9)
6.021***
(0.23)
-0.003**
(0.001)
-0.008***
(0.001)
-2.320***
(0.586)

71 

Table 9. Multilevel regressions, all MS, for non-EU migrants 

(1)

(2)

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

0.031
(0.1)
-0.401***
(0.048)
-0.267
(0.351)
-0.131
(0.195)
0.168
(0.126)
0.049
(0.038)
-0.021
(0.043)
-0.167***
(0.039)
0.025
(0.041)
1.046***
(0.1)
-0.669***
(0.17)
0.379
(0.249)
0.792
(0.411)
-0.46
(0.256)
0.138
(0.215)
0.557**
(0.213)
-0.427
(0.252)
0.104
(0.109)
-0.159*
(0.063)
0.002
(0.166)
5.585***
(0.178)

-3.811***
(0.281)

-0.085
(0.09)
-0.342***
(0.048)
-0.299
(0.378)
-0.298
(0.199)
-0.001
(0.146)
0.025
(0.04)
-0.027
(0.042)
-0.07
(0.036)
-0.038
(0.042)
0.765***
(0.1)
-0.662***
(0.179)
0.269
(0.269)
0.637
(0.417)
-0.691*
(0.282)
0.057
(0.223)
0.105
(0.202)
-0.304
(0.273)
0.094
(0.11)
-0.073
(0.074)
0.195
(0.185)
5.669***
(0.207)
-0.003***
(0.001)
-0.007***
(0.001)
-2.012***
(0.529)

72 

2.  Country-by-country 

Table 10. Multilevel regressions, country-by-country, for EU migrants 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

eu_AT_logit

eu_AT_

eu_AT_pv eu_BE_logit

eu_BE_

eu_BE_pv eu_CZ_logit

eu_CZ_

eu_CZ_pv

0.004
(0.27)
-0.058
(0.22)
0.415
(1.263)
0.487
(0.751)
-0.354
(0.878)
0.116
(0.096)
-0.403***
(0.089)
-0.441***
(0.134)
0.351*
(0.141)
1.216***
(0.326)
-0.157
(0.865)
-0.677
(0.347)
11.681***
(1.489)
-12.958***
(1.271)
0.147
(0.33)

0.39
(1.006)
0.161
(0.166)
0.028
(0.46)
-1.119
(1.218)
30.727***
(2.637)

-0.072
(0.291)
-0.21
(0.348)
0.512
(1.146)
-0.262
(1.067)
-1.055
(1.045)
0.058
(0.078)
-0.438***
(0.115)
-0.417*
(0.19)
0.523**
(0.174)
1.144*
(0.454)
0.849
(1.089)
-0.913*
(0.396)
13.059***
(1.767)
-15.027***
(1.383)
0.374
(0.384)

2.194
(1.21)
0.079
(0.245)
0.332
(0.528)
-0.257
(1.323)
33.797***
(2.997)

-5.290***
(0.791)

-6.550***
(1.042)

-0.22
(0.299)
-0.221
(0.359)
0.412
(1.28)
-0.192
(1.033)
-1.107
(1.07)
0.06
(0.08)
-0.445***
(0.117)
-0.294
(0.188)
0.526**
(0.175)
0.986*
(0.486)
0.987
(1.091)
-0.751
(0.469)
12.541***
(1.712)
-13.938***
(1.48)
0.371
(0.392)

2.015
(1.245)
0.154
(0.259)
-0.03
(0.547)
-0.035
(1.305)
34.522***
(3.331)
0.003
(0.005)
-0.007*
(0.003)
-8.110**
(2.746)

-0.131
(0.198)
0.035
(0.124)
-0.493
(0.973)
-0.723
(0.971)
-0.277
(0.356)
-0.098
(0.095)
-0.320*
(0.141)
-0.317**
(0.1)
0.178
(0.104)
1.038***
(0.204)
-0.684*
(0.336)

-0.357
(0.343)
1.600*
(0.666)
-0.098
(0.207)

-0.32
(0.245)
0.003
(0.149)
-1.158
(0.95)
-0.944
(1.099)
-0.602
(0.449)
-0.052
(0.081)
-0.309*
(0.139)
-0.328*
(0.136)
0.147
(0.135)
1.167***
(0.274)
-0.661*
(0.315)
0
(.)
-0.67
(0.41)
1.883**
(0.728)
-0.021
(0.201)

1.008
(1.023)
-0.62
(0.393)
0.781**
(0.255)
1.285
(0.767)
17.996***
(1.875)

1.338
(1.123)
-0.828
(0.479)
0.779*
(0.324)
2.136**
(0.652)
15.382***
(2.366)

-4.451***
(0.359)

-4.385***
(0.392)

-0.449
(0.243)
0.046
(0.151)
-1.14
(0.969)
-1.027
(1.119)
-0.673
(0.478)
-0.055
(0.082)
-0.311*
(0.137)
-0.26
(0.144)
0.112
(0.137)
1.024***
(0.288)
-0.448
(0.362)
0
(.)
-0.868*
(0.424)
1.808*
(0.786)
0.012
(0.218)

1.295
(1.169)
-0.821
(0.498)
0.063
(0.507)
2.321**
(0.725)
17.682***
(2.515)
0.005
(0.004)
-0.005**
(0.002)
-7.011**
(2.132)

-1.316
(0.709)
-1.487***
(0.431)
-53.641
(.)
0
(.)
1.617
(0.888)
0.745
(.)
-0.48
(0.439)
-0.576
(0.307)
0.546
(0.283)
1.754*
(0.789)
1.418
(0.983)

-1.32
(0.762)
-1.408**
(0.484)
-29.125
(37.03)
0
(.)
0.859
(1.034)
0.014
(0.953)
-0.246
(0.516)
-0.794*
(0.374)
0.876**
(0.324)
2.112*
(0.915)
1.696
(1.272)

-1.296
(0.766)
-1.283**
(0.447)
-31.647
(.)
0
(.)
1.264
(0.984)
-0.085
(.)
-0.091
(0.463)
-0.843*
(0.368)
0.829**
(0.291)
2.019*
(0.853)
0.292
(0.886)

12.040***
(0.697)

10.873***
(0.812)

16.157***
(1.5)

-12.559***
(0.801)

-11.801***
(0.887)

-14.332***
(0.99)

0
(.)
-38.904
(.)
-0.253
(0.641)
-2.123
(8.167)
42.459***
(4.343)

0
(.)
-20.603
(34.999)
-0.033
(0.761)
2.876
(6.845)
43.601***
(5.137)

-8.307***
(0.843)

-8.782***
(1.082)

0
(.)
-20.866
(.)
-1.35
(0.941)
12.079
(8.584)
50.769***
(8.042)
0.021*
(0.009)
-0.006
(0.009)
-19.530***
(5.614)

73 

 
 
 
Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

eu_FI_logit

eu_FI_

eu_FI_pv

eu_IE_logit

eu_IE_

eu_IE_pv

eu_IT_logit

eu_IT_

eu_IT_pv eu_LU_logit

eu_LU_

eu_LU_pv

-0.082
(0.085)
-0.453***
(0.069)
-1.697
(1.833)
-0.176
(0.879)
-0.354
(0.305)
-0.042
(0.119)
-0.139**
(0.048)
-0.176***
(0.047)
0.051
(0.039)
0.558*
(0.227)
0
(.)

-0.203
(0.106)
-0.358***
(0.082)
-2.014
(1.825)
0.027
(0.843)
0.035
(0.446)
-0.008
(0.123)
-0.168**
(0.054)
-0.170***
(0.05)
0.059
(0.05)
0.698*
(0.309)
0
(.)

-0.171
(0.111)
-0.253**
(0.08)
-2.085
(1.841)
-0.319
(0.98)
-0.014
(0.47)
-0.026
(0.124)
-0.178**
(0.056)
-0.046
(0.052)
0.017
(0.047)
0.312
(0.296)
0
(.)

0.210*
(0.097)
-0.381***
(0.072)
-0.5
(0.543)
-0.057
(0.486)
-12.241***
(0.416)
0.109
(0.075)
-0.012
(0.048)
-0.208***
(0.063)
0.133*
(0.065)
-0.146
(0.19)
-3.479***
(0.591)

0.179
(0.125)
-0.437***
(0.077)
-0.454
(0.63)
-0.39
(0.538)
-14.460***
(0.46)
0.111
(0.085)
-0.015
(0.054)
-0.237**
(0.088)
0.192*
(0.081)
-0.185
(0.193)
-3.427***
(0.575)

0.145
(0.124)
-0.298***
(0.082)
-0.539
(0.578)
-0.532
(0.544)
-15.131***
(0.562)
0.139
(0.083)
-0.061
(0.059)
-0.052
(0.08)
0.141
(0.083)
-0.323
(0.182)
-3.427***
(0.581)

0.339
(0.674)
1.387
(0.899)

0.337
(0.675)
0.802
(1.001)

0.347
(0.68)
0.46
(0.991)

0.039
(0.298)
-0.272
(0.193)
-11.024
(6.676)
-6.356
(3.31)
0.416
(0.371)
0.807
(0.415)
-0.254
(0.189)
-0.447**
(0.169)
0.268*
(0.118)
1.030***
(0.306)
2.087
(1.707)

-0.068
(0.466)
-0.368
(0.315)
-27.867*
(11.502)
-12.271***
(2.407)
0.202
(0.614)
1.765**
(0.638)
0.007
(0.379)
-0.339
(0.258)
0.286
(0.174)
1.448*
(0.608)
1.75
(1.236)

0.052
(0.469)
-0.351
(0.326)
-29.192*
(12.945)
-12.418***
(2.454)
0.214
(0.609)
1.847*
(0.717)
0.012
(0.375)
-0.351
(0.237)
0.29
(0.17)
1.398*
(0.663)
1.296
(1.308)

-0.778
(0.599)

-0.491
(0.636)

-0.527
(0.61)

0.305
(0.333)

-0.106
(0.577)

0.235
(0.544)

2.33
(1.303)
0.362
(0.517)
-0.04
(0.092)
1.89
(0.975)
7.349***
(0.436)

2.089
(1.492)
0.214
(0.581)
-0.154
(0.12)
1.986
(1.232)
6.933***
(0.533)

-3.002***
(0.107)

-2.865***
(0.135)

2.189
(1.513)
0.327
(0.62)
-0.266*
(0.135)
3.174*
(1.46)
7.274***
(0.501)
0.001
(0.001)
-0.005***
(0.001)
-3.587***
(0.622)

13.601***
(2.3)
0.34
(0.267)
0.03
(0.108)
0.927
(1.147)
8.395***
(0.547)

13.864***
(2.266)
0.364
(0.357)
0.153
(0.14)
0.308
(1.658)
7.970***
(0.656)

-2.722***
(0.109)

-2.624***
(0.143)

14.644***
(2.389)
0.255
(0.363)
0.159
(0.186)
0.622
(1.62)
8.398***
(0.604)
-0.001
(0.002)
-0.007***
(0.001)
-2.202**
(0.835)

-1.602
(1.456)
-2.010*
(0.862)
0.589
(0.315)
4.098*
(2.041)
18.912***
(2.658)

0.675
(1.726)
-2.944*
(1.276)
0.221
(0.462)
5.962*
(2.316)
16.273***
(2.829)

-7.942***
(1.744)

-7.390***
(1.332)

0.172
(1.781)
-3.475*
(1.595)
0.01
(0.486)
6.127**
(2.258)
17.775***
(3.032)
0.005
(0.004)
-0.004*
(0.002)
-9.862***
(2.646)

0.242
(0.173)
-0.224**
(0.08)
-1.533*
(0.667)
-0.886
(0.464)
-0.193
(0.381)
0.022
(0.038)
-0.346***
(0.092)
-0.181**
(0.068)
0.240***
(0.063)
0.275
(0.144)
-1.637***
(0.288)
0.373
(0.572)
-0.152
(0.913)

0.194
(0.433)
1.053***
(0.259)
0.791
(0.538)
0.263**
(0.097)
0.582**
(0.202)
0.49
(0.385)
8.773***
(1.035)

0.242
(0.173)
-0.224**
(0.08)
-1.533*
(0.667)
-0.886
(0.464)
-0.193
(0.381)
0.022
(0.038)
-0.346***
(0.092)
-0.181**
(0.068)
0.240***
(0.063)
0.275
(0.144)
-1.637***
(0.288)
0.373
(0.572)
-0.152
(0.913)

0.194
(0.433)
1.053***
(0.259)
0.791
(0.538)
0.263**
(0.097)
0.582**
(0.202)
0.49
(0.385)
8.773***
(1.035)

-3.213***
(0.433)

-3.213***
(0.433)

0.177
(0.17)
-0.210**
(0.08)
-1.620*
(0.656)
-0.941*
(0.476)
-0.247
(0.378)
0.027
(0.036)
-0.352***
(0.092)
-0.128
(0.068)
0.227***
(0.062)
0.198
(0.143)
-1.495***
(0.302)
0.299
(0.563)
-0.268
(0.932)

0.208
(0.442)
0.824**
(0.252)
0.719
(0.544)
0.283**
(0.098)
-0.029
(0.217)
0.52
(0.364)
10.917***
(1.293)
0.006**
(0.002)
-0.004***
(0.001)
-5.975***
(1.067)

74 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

eu_PT_logit

eu_PT_

eu_PT_pv

-0.364*
(0.148)
-0.572***
(0.099)
9.194***
(1.718)
0.595
(0.717)
-1.441*
(0.596)
-6.562***
(1.564)
-0.087
(0.07)
-0.156*
(0.075)
0.175**
(0.068)
0.159
(0.374)
-4.591***
(0.823)
2.214***
(0.536)
0
(.)
-0.183
(0.299)
3.478***
(0.534)

0.018
(1.347)
0.281
(0.342)
0.088
(0.154)
0.5
(2.767)
8.061***
(0.926)

-0.235
(0.178)
-0.702***
(0.123)
13.794***
(1.992)
0.77
(0.858)
-2.801***
(0.736)
-10.220***
(1.762)
-0.033
(0.076)
-0.195*
(0.096)
0.254**
(0.079)
0.085
(0.499)
-4.776***
(1.02)
2.468***
(0.591)
0
(.)
-0.242
(0.402)
3.573***
(0.582)

1.488
(1.496)
-0.017
(0.338)
-0.243
(0.19)
-0.991
(4.356)
7.329***
(0.947)

-2.822***
(0.341)

-3.265***
(0.455)

-0.371*
(0.184)
-0.647***
(0.119)
14.058***
(2.076)
0.924
(0.853)
-2.823***
(0.759)
-10.497***
(1.828)
0.022
(0.082)
-0.149
(0.103)
0.230**
(0.085)
-0.158
(0.503)
-5.079***
(1.031)
2.833***
(0.59)
0
(.)
-0.558
(0.393)
3.651***
(0.601)

1.077
(1.592)
0.127
(0.341)
-0.621**
(0.204)
0.131
(3.886)
8.248***
(1.107)
0.004
(0.003)
-0.007***
(0.001)
-5.310***
(1.482)

Note: Only those MS are shown for which the models converged. 

75 

Table 11. Multilevel regressions, country-by-country, for non-EU migrants 

neu_BE_logit

neu_BE_ neu_BE_pv

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

0.023
(0.191)
0.154
(0.113)
1.154
(0.79)
-0.297
(0.33)
-0.252
(0.357)
-0.103
(0.079)
-0.327*
(0.147)
-0.361***
(0.092)
0.147
(0.111)
0.982***
(0.203)
-0.889**
(0.294)

-0.478
(0.336)
1.451*
(0.639)
-0.024
(0.205)

-0.696
(0.556)
-0.281
(0.268)
0.644*
(0.258)
-0.485
(0.568)
18.434***
(1.901)

-0.143
(0.248)
0.188
(0.14)
0.667
(1.028)
-0.132
(0.368)
-0.624
(0.457)
-0.08
(0.1)
-0.319*
(0.15)
-0.388***
(0.116)
0.095
(0.138)
1.093***
(0.266)
-0.897**
(0.285)
0
(.)
-0.821*
(0.386)
2.193**
(0.705)
0.149
(0.217)

0.071
(0.589)
-0.235
(0.27)
0.62
(0.333)
-0.236
(0.692)
15.871***
(2.195)

-4.230***
(0.351)

-4.160***
(0.378)

-0.25
(0.245)
0.223
(0.142)
0.755
(1.035)
-0.225
(0.358)
-0.702
(0.483)
-0.111
(0.104)
-0.318*
(0.148)
-0.329**
(0.124)
0.059
(0.14)
0.984***
(0.284)
-0.727*
(0.313)
0
(.)
-0.961*
(0.391)
2.244**
(0.756)
0.179
(0.234)

0.084
(0.597)
-0.259
(0.267)
0.154
(0.465)
0.008
(0.71)
17.352***
(2.265)
0.003
(0.004)
-0.004**
(0.002)
-5.931**
(1.941)

76 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

neu_FI_logit

neu_FI_

neu_FI_pv neu_IT_logit

neu_IT_

neu_IT_pv neu_LU_logit

neu_LU_ neu_LU_pv

-0.106
(0.082)
-0.442***
(0.066)
0.772
(0.794)
-0.307
(0.536)
-0.359
(0.304)
-0.126
(0.092)
-0.140**
(0.048)
-0.174***
(0.048)
0.066
(0.038)
0.471*
(0.218)
0
(.)

-0.233*
(0.105)
-0.365***
(0.078)
1.767
(0.903)
-0.202
(0.517)
0.029
(0.446)
-0.243*
(0.107)
-0.169**
(0.055)
-0.157**
(0.048)
0.071
(0.048)
0.614*
(0.298)
0
(.)

0.221
(0.655)
0.257
(0.216)
-0.004
(0.093)
0.034
(0.395)
7.466***
(0.473)

-0.396
(0.853)
0.281
(0.228)
-0.134
(0.12)
0.038
(0.455)
7.045***
(0.555)

-3.008***
(0.116)

-2.865***
(0.141)

-0.195
(0.11)
-0.254***
(0.076)
0.88
(0.77)
-0.766
(0.515)
-0.039
(0.47)
-0.204*
(0.097)
-0.180**
(0.056)
-0.033
(0.049)
0.023
(0.046)
0.248
(0.281)
0
(.)

-0.14
(0.848)
0.37
(0.21)
-0.225
(0.135)
0.462
(0.442)
7.290***
(0.537)
0.001
(0.001)
-0.005***
(0.001)
-3.331***
(0.604)

-0.122
(0.272)
-0.312
(0.177)
-0.852
(1.407)
0.646
(0.575)
0.368
(0.38)
0.135
(0.122)
-0.224
(0.196)
-0.461**
(0.151)
0.302**
(0.107)
0.873**
(0.28)
2.400*
(1.221)

-0.106
(0.424)
-0.369
(0.276)
-3.032*
(1.533)
-0.714
(0.857)
0.126
(0.601)
0.227
(0.134)
0.068
(0.358)
-0.384
(0.225)
0.345*
(0.162)
1.379*
(0.537)
1.1
(1.568)

0.004
(0.411)
-0.348
(0.289)
-3.241
(1.694)
-0.87
(0.869)
0.146
(0.594)
0.229
(0.143)
0.084
(0.346)
-0.397
(0.206)
0.357*
(0.158)
1.313*
(0.597)
0.58
(1.539)

-0.598
(0.644)

0.157
(0.567)

0.136
(0.561)

0.235
(0.324)

0.091
(0.535)

0.426
(0.496)

-0.344
(0.668)
-0.773*
(0.352)
0.589*
(0.296)
0.807
(1.046)
19.125***
(2.462)

0.954
(0.979)
-1.116*
(0.52)
0.375
(0.437)
-0.224
(1.446)
14.809***
(3)

-8.079***
(1.2)

-6.590***
(1.731)

0.861
(1.004)
-1.157*
(0.576)
0.136
(0.462)
-0.327
(1.46)
16.200***
(3.155)
0.006
(0.004)
-0.005*
(0.002)
-9.046***
(2.624)

0.376*
(0.178)
-0.387**
(0.118)
-1.704
(1.221)
-0.592
(0.615)
-0.21
(0.378)
0.057
(0.118)
-0.351***
(0.097)
-0.159
(0.093)
0.145
(0.082)
0.329
(0.236)
-1.629***
(0.311)
-0.502
(1.014)
-0.002
(0.916)

0.526
(0.435)
0.541*
(0.251)
0.275
(0.625)
0.346
(0.268)
0.484*
(0.194)
0.907*
(0.44)
11.671***
(1.729)

0.376*
(0.178)
-0.387**
(0.118)
-1.704
(1.221)
-0.592
(0.615)
-0.21
(0.378)
0.057
(0.118)
-0.351***
(0.097)
-0.159
(0.093)
0.145
(0.082)
0.329
(0.236)
-1.629***
(0.311)
-0.502
(1.014)
-0.002
(0.916)

0.526
(0.435)
0.541*
(0.251)
0.275
(0.625)
0.346
(0.268)
0.484*
(0.194)
0.907*
(0.44)
11.671***
(1.729)

-3.473***
(0.393)

-3.473***
(0.393)

0.303
(0.183)
-0.361**
(0.12)
-1.624
(1.225)
-0.665
(0.63)
-0.254
(0.372)
0.041
(0.125)
-0.358***
(0.097)
-0.107
(0.091)
0.139
(0.082)
0.278
(0.238)
-1.552***
(0.321)
-0.524
(1.01)
-0.099
(0.953)

0.564
(0.448)
0.366
(0.245)
0.26
(0.633)
0.354
(0.278)
-0.275
(0.319)
1.077*
(0.437)
14.327***
(1.95)
0.008*
(0.004)
-0.003**
(0.001)
-7.407***
(1.955)

77 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

neu_PT_logit

neu_PT_ neu_PT_pv neu_UK_logit

-0.374**
(0.144)
-0.536***
(0.105)
0.098
(0.704)
-0.095
(0.541)
-1.455*
(0.594)
-0.141
(0.084)
-0.092
(0.07)
-0.151*
(0.073)
0.169**
(0.065)
0.226
(0.365)
-4.272***
(0.828)
1.958***
(0.559)
0
(.)
-0.219
(0.312)
3.227***
(0.552)

2.944**
(0.928)
0.427
(0.438)
0.185
(0.143)
-0.558
(0.501)
8.278***
(0.931)

-0.128
(0.254)
-0.470*
(0.197)
0.138
(0.826)
0.589
(0.64)
-2.881***
(0.736)
-0.217
(0.152)
-0.047
(0.071)
-0.144
(0.082)
0.174
(0.1)
0.391
(0.587)
-3.911***
(1.101)
1.773**
(0.639)
0
(.)
-0.153
(0.408)
3.020***
(0.586)

4.347***
(1.143)
0.278
(0.513)
-0.015
(0.197)
0.261
(0.5)
7.587***
(1.01)

-2.773***
(0.356)

-3.347***
(0.553)

-0.188
(0.29)
-0.414*
(0.2)
0.301
(0.821)
0.507
(0.706)
-2.834***
(0.764)
-0.213
(0.145)
-0.008
(0.07)
-0.109
(0.084)
0.142
(0.107)
0.234
(0.625)
-4.137***
(0.995)
1.985***
(0.585)
0
(.)
-0.357
(0.417)
3.047***
(0.554)

4.077***
(1.126)
0.195
(0.548)
-0.335
(0.211)
0.528
(0.507)
8.502***
(1.279)
0.005
(0.003)
-0.005*
(0.002)
-5.737**
(1.834)

-0.205
(0.319)
-0.629***
(0.189)
-0.613
(0.734)
-0.872
(1.067)
1.801**
(0.589)
0.041
(0.067)
-0.573**
(0.221)
-0.254
(0.163)
0.26
(0.166)
0.128
(0.473)
-13.305***
(0.921)
0
(.)
12.066***
(0.306)

-0.131
(0.483)

-0.804
(0.866)
0.144
(0.342)
0.053
(0.328)
0.748
(0.751)
30.993***
(4.261)

-4.727***
(0.913)

78 

B.  Direct comparison of EU to non-EU immigrants 

These  regressions  do  not  consider  natives,  but  compare  directly  EU  migrants  to  non-EU 
immigrants.  The  baseline  category  are  EU  migrants.  Thus,  if  there  is  a  significant 
coefficient, it means that there is a significant difference between non-EU immigrants to 
the baseline category which are EU migrants. Otherwise, the regressions follow the same 
specifications as the previous  ones. Note that  we do not show  regressions for those MS 
for which the models did not achieve convergence (usually due to relatively small sample 
size and small variation in the variables). 

79 

Table 12. Multilevel regressions, all MS, baseline category: EU migrants 

(1)

(2)

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

-0.129
(0.247)
-0.252*
(0.125)
-0.474
(0.452)
0.000
(.)
-0.120
(0.441)
0.045
(0.041)
-0.696*
(0.307)
-0.247**
(0.092)
-0.065
(0.100)
0.940***
(0.265)
-0.146
(0.350)
0.011
(0.500)
0.587
(0.741)
-0.413
(0.519)
0.255
(0.508)
1.013**
(0.367)
0.336
(0.484)
0.785*
(0.322)
-0.217
(0.149)
0.207
(0.604)
6.887***
(0.374)

-4.308***
(0.796)

-0.214
(0.250)
-0.225
(0.124)
-0.440
(0.470)
0.000
(.)
-0.292
(0.459)
0.025
(0.042)
-0.834*
(0.334)
-0.162
(0.099)
-0.102
(0.097)
0.769**
(0.270)
-0.388
(0.351)
-0.048
(0.512)
0.558
(0.725)
-0.695
(0.553)
0.418
(0.514)
0.586
(0.382)
0.448
(0.475)
0.903**
(0.347)
-0.245
(0.146)
0.026
(0.591)
7.487***
(0.430)
0.001
(0.002)
-0.008***
(0.002)
-4.255***
(1.181)

80 

Table 13. Multilevel regressions, country-by-country, baseline category: EU migrants 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

_AT_logit

_AT_

_AT_pv

_BE_logit

_BE_

_BE_pv

_DE_logit

_DE_

_DE_pv

_FI_logit

_FI_

_FI_pv

-1.378
(0.820)
-0.443
(0.485)
0.355
(1.153)
0.000
(.)
0.502
(1.121)
0.021
(0.072)
-0.188
(0.301)
-0.704
(0.400)
-0.689
(0.464)
1.559*
(0.749)
-1.492
(0.961)
0.284
(0.685)
14.419***
(1.402)
-14.481***
(1.340)
0.591
(0.931)

-1.754
(1.099)
0.250
(0.414)
-0.148
(0.584)
0.088
(0.746)
13.507***
(2.039)

-2.137
(1.300)
-0.680
(0.867)
-0.015
(1.835)
0.000
(.)
1.457
(1.867)
-0.057
(0.088)
-0.325
(0.713)
-1.222
(0.746)
-1.229
(0.815)
2.241
(1.410)
-2.677
(1.509)
-0.140
(1.040)
15.384***
(1.613)
-15.049***
(1.792)
1.264
(1.355)

-4.059
(2.123)
0.292
(0.881)
-0.171
(0.893)
0.725
(1.164)
18.737***
(5.151)

-4.647***
(1.226)

-5.181*
(2.486)

-2.760
(1.699)
-0.242
(0.927)
0.143
(1.976)
0.000
(.)
1.255
(2.425)
-0.107
(0.156)
-0.234
(0.476)
-1.152
(0.740)
-1.107
(0.686)
2.064
(1.318)
-2.203
(1.889)
-1.301
(1.434)
13.347***
(1.690)
-15.712***
(1.523)
0.287
(1.195)

-4.557
(2.515)
0.105
(0.616)
0.565
(1.264)
1.022
(1.781)
20.211**
(7.004)
-0.019
(0.012)
-0.017
(0.011)
4.141
(5.528)

0.708
(0.644)
0.579
(0.331)
1.238
(0.703)
0.000
(.)
0.183
(0.761)
-0.185
(0.109)
-0.491
(0.451)
-0.569**
(0.202)
-0.136
(0.239)
1.649*
(0.733)
-0.355
(0.594)

-2.854**
(1.067)
2.207
(1.182)
-0.007
(0.632)

-1.503
(0.968)
-0.278
(0.517)
0.082
(0.325)
0.461
(0.751)
17.986***
(2.801)

0.606
(0.708)
0.821*
(0.365)
0.924
(0.823)
0.000
(.)
0.257
(0.802)
-0.192
(0.132)
-0.685
(0.543)
-0.703**
(0.239)
-0.290
(0.319)
1.479*
(0.734)
-0.520
(0.617)
0.000
(.)
-4.819***
(1.037)
2.192
(1.326)
0.369
(0.643)

-1.466
(1.123)
0.013
(0.575)
-0.050
(0.383)
0.278
(0.876)
15.889***
(2.913)

-6.336***
(1.299)

-5.762***
(1.333)

0.576
(0.705)
0.883*
(0.377)
1.075
(0.769)
0.000
(.)
0.143
(0.804)
-0.209
(0.126)
-0.756
(0.575)
-0.555*
(0.249)
-0.351
(0.339)
1.432
(0.784)
-0.133
(0.585)
0.000
(.)
-5.048***
(1.103)
2.210
(1.471)
0.157
(0.604)

-1.392
(1.117)
0.150
(0.613)
0.152
(0.419)
0.127
(0.893)
16.211***
(3.079)
-0.006
(0.007)
-0.004
(0.004)
-2.832
(3.928)

0.254
(0.299)
-0.544**
(0.173)
0.042
(0.756)
0.000
(.)
-0.209
(0.515)
-0.029
(0.094)
-0.466
(0.307)
-0.241
(0.127)
0.119
(0.143)
0.401
(0.330)
-11.920***
(0.707)
12.330***
(0.742)

0.000
(.)

0.358
(0.604)
0.459
(0.336)
-0.099
(0.294)
0.611
(0.590)
6.592***
(0.497)

0.294
(0.397)
-0.676***
(0.192)
0.787
(0.761)
0.000
(.)
-0.159
(0.509)
-0.130
(0.093)
-0.766
(0.413)
-0.231
(0.138)
0.244
(0.174)
0.469
(0.372)
-12.455***
(0.714)
12.845***
(0.772)

0.000
(.)
0.000
(.)

0.439
(0.606)
0.844
(0.446)
0.032
(0.372)
0.094
(0.704)
7.000***
(0.730)

-4.421***
(0.647)

-4.301***
(0.75)

0.179
(0.409)
-0.692***
(0.175)
0.850
(0.735)
0.000
(.)
-0.543
(0.663)
-0.162
(0.083)
-0.988*
(0.480)
-0.029
(0.170)
0.174
(0.198)
0.197
(0.326)
-12.516***
(0.878)
12.816***
(0.934)

0.000
(.)
0.000
(.)

0.612
(0.680)
1.027*
(0.487)
0.143
(0.334)
-0.172
(0.682)
8.123***
(0.907)
0.001
(0.003)
-0.011**
(0.004)
-4.367*
(1.717)

-0.087
(0.527)
-0.403
(0.314)
0.589
(0.748)
0.000
(.)
-0.726
(1.027)
-0.067
(0.074)
-0.113
(0.285)
0.044
(0.275)
0.084
(0.274)
-0.738
(1.056)
0.000
(.)

-0.369
(0.533)
-0.567
(0.326)
0.427
(0.768)
0.000
(.)
-0.033
(1.003)
-0.067
(0.091)
-0.018
(0.285)
0.174
(0.284)
0.030
(0.270)
-0.862
(1.208)
0.000
(.)

0.858
(1.076)
0.673
(0.442)
0.405
(0.261)
-2.114*
(0.962)
9.087***
(1.326)

0.464
(1.014)
0.578
(0.413)
0.399
(0.303)
-2.083*
(0.921)
9.233***
(1.263)

-2.294***
(0.583)

-2.442***
(0.587)

-0.216
(0.504)
-0.272
(0.358)
0.084
(0.808)
0.000
(.)
0.349
(1.223)
-0.049
(0.097)
0.234
(0.387)
0.410
(0.341)
-0.010
(0.285)
-1.201
(1.268)
0.000
(.)

0.035
(1.035)
0.291
(0.455)
0.651*
(0.285)
-1.745
(0.894)
9.292***
(1.691)
-0.004
(0.004)
-0.008*
(0.004)
-0.647
(1.631)

81 

Gender

Student's ESCS

1st generation

2nd generation

Language at home

Age at arrival

Sense of belonging

Epist. beliefs

Truancy

Grade repetition

ISCED level: 3

Designation: B

Designation: C

Orientation: Pre-Voc.

Orientation: Voc.

Orientation: Modular

I: Immig. x Lang. at home

I: Immig. x Sense of belong.

School's ESCS

School's conc. of mig.

School's exp. ESL level

School's science score

Student's science score

Constant

_LU_logit

_LU_

_LU_pv

_UK_logit

_UK_

_UK_pv

0.134
(0.145)
-0.218*
(0.097)
-0.736
(0.410)
0.000
(.)
0.597
(0.381)
0.030
(0.039)
-0.078
(0.092)
-0.214*
(0.099)
0.213**
(0.076)
0.286
(0.173)
-1.486**
(0.485)
0.091
(0.830)
0.000
(.)

0.216
(0.610)
1.010**
(0.377)
-0.148
(0.257)
0.062
(0.228)
0.941***
(0.274)
-1.181
(0.989)
10.628***
(1.281)

0.134
(0.145)
-0.218*
(0.097)
-0.736
(0.410)
0.000
(.)
0.597
(0.381)
0.030
(0.039)
-0.078
(0.092)
-0.214*
(0.099)
0.213**
(0.076)
0.286
(0.173)
-1.486**
(0.485)
0.091
(0.830)
0.000
(.)

0.216
(0.610)
1.010**
(0.377)
-0.148
(0.257)
0.062
(0.228)
0.941***
(0.274)
-1.181
(0.989)
10.628***
(1.281)

-3.257***
(0.441)

-3.257***
(0.441)

0.087
(0.139)
-0.218*
(0.100)
-0.730
(0.409)
0.000
(.)
0.464
(0.376)
0.034
(0.038)
-0.063
(0.093)
-0.166
(0.112)
0.192**
(0.073)
0.209
(0.167)
-1.381**
(0.499)
-0.034
(0.812)
0.000
(.)

0.339
(0.612)
0.797*
(0.393)
-0.138
(0.253)
0.048
(0.232)
0.480
(0.307)
-1.060
(1.019)
12.056***
(1.292)
0.004*
(0.002)
-0.005**
(0.001)
-5.213***
(1.183)

0.151
(0.868)
0.070
(0.777)
0.963
(1.220)
0.000
(.)
-0.057
(1.020)
-0.215
(0.118)
1.056*
(0.453)
-0.483
(0.389)
0.079
(0.286)
3.165
(1.691)
15.393***
(2.264)
-14.854***
(2.179)
0.000
(.)
-3.879***
(0.937)
-12.357***
(1.440)

2.388
(1.456)
-0.704
(0.767)
0.526
(0.474)
0.577
(1.311)
12.432***
(2.092)

0.459
(1.245)
1.091
(0.661)
-0.133
(2.314)
0.000
(.)
0.557
(1.403)
-0.217
(0.249)
0.707
(0.729)
0.056
(0.550)
-0.472
(0.312)
4.174
(2.291)
20.917***
(2.330)
-20.126***
(2.386)
0.000
(.)
-3.513**
(1.151)
-17.338
(.)

2.760
(1.758)
0.131
(0.867)
-0.319
(0.880)
0.159
(1.420)
12.263***
(1.849)

-7.685**
(2.736)

-8.773**
(3.179)

0.875
(1.204)
1.307
(0.832)
-0.635
(2.055)
0.000
(.)
-0.537
(2.043)
-0.239
(0.213)
1.137
(2.012)
0.176
(0.680)
-0.468
(0.385)
5.018*
(1.977)
16.917***
(1.809)
-17.610***
(2.371)
0.000
(.)
-3.711*
(1.511)
-13.993
(.)

4.644*
(1.876)
0.081
(1.954)
-1.701
(1.293)
0.951
(1.673)
16.969***
(3.997)
0.031*
(0.014)
-0.004
(0.009)
-26.121**
(9.408)

82 

K
J
-
N
A
-
2
8
8
6
6
-
E
N
-
N

doi:10.2760/111445 

ISBN 978-92-79-76328-1 

83