Identificational assimilation patterns in young first, second, 2.5 and third generation

migrants

Floor Verhaeghe

Department of Social Work and Social Pedagogy, CESSMIR, Ghent University, Ghent, BelgiumHenri Dunantlaan 2, 9000 GentP 09 264 62 92F 09 264 64 93Floor. Verhaeghe@UGent.be

Floor Verhaeghe is currently working on a PhD about assimilation processes in an inter- and multigenerational perspective. The PhD focuses on various aspects of assimilation, such as structural inequalities, language loss, diversity in social networks, identifications and belonging.

Lieve Bradt

Department of Social Work and Social Pedagogy, Ghent University, Ghent, BelgiumHenri Dunantlaan 2, 9000 Gent P 09 264 63 82F 09 264 64 93Lieve.Bradt@UGent.be

Lieve Bradt obtained her PhD in Educational Sciences at Ghent University. Since 2016 she is Professor of Social Pedagogy at the Department of Social Work and Social Pedagogy at Ghent University. Her research interests concern youth research with a particular focus on inclusion and exclusion processes of young people within school and leisure contexts. She is the coordinator of the Youth Research Platform, an interdisciplinary and interuniversity policy research centre subsidized by the Flemish Government, which conducts large-scale survey research on the life-conditions, attitudes and activities of young people in Flanders.

Mieke Van Houtte

Research Group CuDOS, Department of Sociology, Ghent University, Ghent, BelgiumKorte Meer 5, 9000 Gent P 09 264 68 02F 09 264 69 75Mieke.Vanhoutte@UGent.be

Mieke Van Houtte is full professor and head of the research team CuDOS (Department of Sociology, Ghent University, Belgium). Her research interests cover diverse topics within the sociology of education, particularly the effects of structural and compositional school features

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on several diverse outcomes for students and teachers, and sexual minorities. She is a member of the Royal Flemish Academy of Belgium for Science and the Arts.

Ilse Derluyn

Department of Social Work and Social Pedagogy, CESSMIR, Ghent University, Ghent, BelgiumHenri Dunantlaan 2, 9000 Gent P 09 264 64 02F 09 264 64 93Ilse.Derluyn@UGent.be

Ilse Derluyn obtained her PhD in Educational Sciences at Ghent University (Belgium) and is currently affiliated as lecturer to the Department of Social Work and Social Pedagogy (Ghent University), where she teaches courses in migration and refugee studies. Ilse's main research topics concern the psychosocial wellbeing of unaccompanied refugee minors, migrant and refugee children, war-affected children, victims of trafficking and child soldiers. She is also actively involved in supporting refugees and practitioners working with refugees and migrants, in policy research and policy-influence. Ilse published over 50 international publications and several books and is holder of an ERC Starting Grant. Prof. Derluyn is heading the Centre for the Social Study of Migration and Refugees (CESSMIR) and is co-director of the Centre for Children in Vulnerable Situations (CCVS).

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Classical assimilation theory states that migrants’ identification with a receiving

country strengthens over generations, whilst identification with the home

country weakens. A Flemish survey of first (G1), second (G2), 2.5 (G2.5) and

third (G3) generation immigrant youths (n=1,587) reveals that the findings for

adolescents with a background in the oldest 15 member states of the European

Union (EU15) seem to align with classical assimilation theory. In the non-EU15

group, those of G2 and G2.5 show the strongest identification with both

countries. The Belgian context also prompted investigation of the identification

with Flanders, which proved to be even more exclusionary than Belgian national

identification. Last, the young people’s identification with their city of residence

was not as strong as expected from literature, probably because it was the

neighbourhood, rather than the city as a whole, that mattered to them. At the

least, nuancing the classical assimilation theory is thus necessary.

Keywords: identificational assimilation; migrant generations; multiple

identifications; national identity; ethnic identity

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Introduction

Classical assimilation theory suggests that migrants’ identification with the majority

group will strengthen over time, overtaking their identification with their home country

(Phinney, 1990; Phinney & Ong, 2007). Straight-line assimilation theory (Warner &

Srole, 1945) suggests this process will occur gradually, over generations. Although

some studies into the self-identification of second and 2.5 generations exist, few have

focused on comparing the first and second or 2.5 generations (for exceptions see Platt,

2014; van Heelsum & Koomen, 2015). However, without such comparisons it is

impossible to examine the changes across generations, and accordingly the straight-line

assimilation theory’s validity.

In addition, we know little about the so-called ‘third generation’. In this regard,

Gans’ (1979) symbolic ethnicity hypothesis argues that ethnic identities are important to

third generation migrants, but that those identities have a somewhat symbolic and

voluntary character. From this viewpoint, the third generation is no longer restricted to

particular ethnic networks or behavioural expectations, but instead looks for new ways

of ‘being ethnic’, often by selecting some symbols (e.g., celebrating certain holidays or

cooking specific foods) to show their ethnic affiliation and pride. Waters (1990) and

Alba (1990) empirically support this hypothesis, however, their research deals with

white Americans of European descent, while the situation for non-whites might be

different. Evidence from at least three generations of different migrant groups is needed

to nuance or invalidate this theory on symbolic ethnicity.

Further to this, much classical assimilation research only considers identification

with the receiving country and the country of origin. In some receiving countries, there

are subnational identifications that might be important or might even compete with

national identification. This is the case in Flanders, the Dutch-speaking region of

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Belgium (Van Nieuwenhuyse & Wils, 2015) and the geographical context of the current

research, but also in, for example, Catalonia, Scotland and Quebec (Keating, 1996).

Since people can have many potential identifications, including those that do not refer to

(ethnic) origin, we include adolescents’ identification with the city they live in, as there

is evidence that the city is important for young people, especially those with a migration

background (Fibbi, Wanner, Topgül, & Ugrina, 2015).

The current paper aims to analyse how these four different identifications (ethnic,

national, subnational and city) evolve over four generations, using empirical data about young

people with a migration background in the context of Flanders, Belgium.

Background

Identity and self-identifications

The concept of identity refers to the question of who one is (Verkuyten, 2005). It entails both

continuous individual processes of constructing a ‘self’, and social processes constantly

(re)negotiating who one is (Brubaker, 2009; Verkuyten, 2005). We focus on the self-

identifications of young people with a migration background, and the ways in which they

identify themselves by referring to specific groups (Verkuyten, 2005). As it is commonly

agreed that identities–and thus self-identifications–are always multiple, we include several

potential levels of identifications simultaneously (Verkuyten, 2005).

The first level comprises ethnic self-identifications, defined by Phinney (1990) as ‘the

psychological relationship of ethnic and racial minority group members with their own group’

(p. 499). To her, ‘ethnic identity’ entails several components: the sense of belonging to a

group, ethnic involvement (social participation, cultural practices and attitudes), attitudes to

one’s own group membership, and self-identification. The last of these, considered as an

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essential starting point in researching ethnic identities, is defined as the way in which people

define themselves in terms of the groups they feel they belong to (Phinney & Ong, 2007).

A second aspect under consideration is identification with the mainstream. In

Belgium, the question of what ‘the mainstream’ is in terms of identifications is somewhat

complex. ‘Belgian identity’ might be a relatively hollow term, because of the ‘absence of a

substantial and coherent Belgian historical narrative’ (Van Nieuwenhuyse & Wils, 2015, p.

61) and the ‘gradual cultural and social divergence between Flanders and Wallonia’ (Billiet,

Maddens, & Frognier, 2006, p. 912). This trend towards cultural divergence has been

institutionalized and at the same time enhanced by subsequent state reforms, in which the

communities were granted more autonomy in domains such as culture and education (Billiet

et al., 2006). On top of that, the economic prosperity of Flanders has turned this region into a

‘textbook case of modern, economic nationalism that can also be found in regions such as

Catalonia or Scotland’ (Boonen & Hooghe, 2014, p. 73), and the Belgian national

consciousness might have been put under pressure by a form of subnational consciousness

(Billiet et al., 2006).

Identification with the city young people live in is the next relevant focus, because

evidence shows the importance of the local (city and neighbourhood) level in youngsters’

identifications, in particular for those with a migrant background (Fibbi et al., 2015). Migrant

children often identify more strongly with the local level than native peers do (Crul &

Schneider, 2010), and more with the local than the national level. This difference is not that

large or can even be the reverse for non-migrants (Schneider et al., 2012). For migrant

children, identification with the city level can be an alternative to the national level, given that

‘discourses stressing non-belonging or the “otherness” of children of immigrants mostly draw

on national representations of belonging’ (Schneider et al., 2012, p. 73).

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Self-identifications over generations: (new) assimilation and segmented assimilation

Early theories on identificational assimilation proposed bipolar models: a strengthening

of the identification with the mainstream was assumed to take place over time, together

with a weakening of ethnic identity (Phinney, 1990; Warner & Srole, 1945). Later on, a

two-dimensional model was established. This empirically confirmed model (Phinney &

Devich-Navarro, 1997) puts the dimension of identification with the majority group

next to and in combination with the dimension of ethnic identification and/or

identification with the ‘home country’ (Berry, 2008; Phinney, 1990). This creates

possibilities for so-called ‘hyphenated’ or ‘transnational’ identities, which are connected

to both the place of origin (or that of the parents/grandparents) and the receiving place

(Colombo, Leonini, & Rebughini, 2009). More broadly, classical assimilation theory

has been criticized for various reasons (see Brubaker, 2001) and has been replaced by

two newer alternatives: the ‘new assimilation theory’ and the ‘segmented assimilation

theory’.

Proponents of the new assimilation theory argue that (identificational) assimilation,

understood as a gradual change towards increasing similarity in some respects and between

some groups, is still widely taking place (Brubaker, 2001). However, they no longer see

assimilation as (solely) taking place at the individual level, but also at the population level

over generations (Brubaker, 2001). The idea is still that, over generations, identifications with

the mainstream become stronger and ethnic identifications become weaker (Brubaker, 2001),

however, rather than a straight-line process, this can be a ‘bumpy line’, with interruptions and

tangents at times (Gans, 1992).

The theory of ‘segmented assimilation’ suggests that there are different pathways

towards assimilation, and further that some people with a migration background will never

assimilate into what is considered the mainstream (Portes & Zhou, 1993). A first pathway is

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assimilation into the mainstream, as described above, in which identificational and

socioeconomic assimilation go hand in hand. In the second pathway, termed ‘downward

assimilation’, people assimilate into a so-called ‘underclass’ instead of the mainstream,

resulting in a low socioeconomic status and a ‘reactive ethnicity’ (Portes & Zhou, 1993). The

latter refers to a ‘thickening’–instead of a thinning–ethnic identity occurring in the second and

2.5 generations (Portes & Rumbaut, 2001). This is said to drive youngsters away from

mainstream society, leading to low school achievement and even involvement in gangs

(Portes & Zhou, 1993). Portes and Zhou (1993) indicate that, for example, being of colour is a

factor that makes migrant children more vulnerable to downward assimilation. Though this

view is criticized for its pessimism and determinism, there is a broad consensus that for some

groups, assimilation is made more difficult because they need to cope with multiple

discriminatory and othering practices (Antonsich, 2012; Meuleman & Billiet, 2003).

A third pathway ‘associates rapid economic advancement with deliberate preservation

of the immigrant community’s values and tight solidarity’ (Portes & Zhou, 1993, p. 82). Here,

a high level of ethnic identification would, for example, keep youngsters close to their family

and their wider ethnic community, which is linked to strong parental discipline and social

control and accordingly leads to better school achievement (Portes, Fernandez-Kelly, &

Haller, 2009; Portes & Zhou, 1993).

Research questions

The current article aims to explore differences in identificational assimilation processes in

adolescents with a migration background over four generations, compared with a group

without a migration background. We examine four–often co-occurring–levels of identification

and start by analysing whether there are differences in the strength of the national

identification. Consistent with the straight-line assimilation hypothesis, we expect a change in

national identification over the generations, possibly reaching a similar identification level

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between the third generation and those without a migration background (hypothesis one).

Second, we check whether these patterns are similar for the subnational identification with

Flanders, and if this identification with Flanders is lower than with Belgium, possibly

resulting in differences between those with and without a migration background up to the

third generation (hypothesis two). Third, we examine whether the ethnic identification

gradually vanishes over generations (hypothesis three). Fourth, we investigate whether

youngsters with a migration background identify more strongly with the city level than non-

migrants do (hypothesis four), and explore how this evolves over generations. Lastly, we

hypothesize that there may be differences between young people with a migration background

who are part of a group that is more vulnerable to discrimination and others who are less (or

not) vulnerable (hypothesis five). We therefore carry out all of the above analyses separately

for youngsters with a migration background within the EU15 on the one hand, and on the

other for youngsters with a migration background outside the EU15. The EU15 countries are:

Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg,

the Netherlands, Portugal, Spain, Sweden and the UK (European Union, 1995-2015). We

make this specific distinction because young people with a background within the EU15 are

perceived as relatively similar to the native population, whereas migrants coming from

countries that joined the EU more recently, together with migrants having a background from

elsewhere in the world, are often construed as ‘the other’ and are more likely to experience

discrimination (Andre & Dronkers, 2017; Meuleman & Billiet, 2003).

Methods

Context and procedure

Between January and May 2014, students from eight secondary schools in Genk and Sint-

Niklaas (four schools in each city) participated in a survey. These two cities were chosen

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because a large number of their inhabitants are migrants and migrant descendants: in Genk,

54.0 per cent of the inhabitants are of foreign origin (the second highest percentage of all

Flemish cities) (Vanduynslager, Wets, Noppe, & Doyen, 2013); in Sint-Niklaas, the figure is

20.4 per cent (Agentschap voor Binnenlands Bestuur en Studiedienst van de Vlaamse

Gemeenschap, 2013). Inhabitants of foreign origin are defined in these statistics as those

whose current or first nationality is not Belgian, or those with at least one parent whose first

nationality is not Belgian (Vanduynslager et al., 2013). The schools were selected because

they are complementary in the educational tracks and disciplines that they offer (general as

well as vocational; trade and industry-related as well as care-related courses).

Within the selected schools, all pupils in the fifth, sixth and (when available) seventh

grade (equivalent to the 11th through 13th grade in the American system, intended for pupils

between 16 and 19 years old) were asked to take part in the research; they all agreed to do so

(n=1,628). The first author spent approximately one week at each school to be able to have at

least one 50-minute period with all the pupils to fill out the questionnaire. First, the researcher

discussed the aim, content, informed consent forms and practicalities of the research with the

pupils. Anonymity of the research was stressed and written informed consent was obtained

from all the participants. The youngsters were given the opportunity to share their contact

details in order to receive information about the results and/or to be contacted later for a

second part of the research. They were given contact details for the researcher, whom they

could contact if they had any further questions or remarks. Film tickets were raffled off as an

incentive. For most of the participants, the surveys were filled out online in the presence of

the researcher and a teacher. The exceptions were 21 pupils who completed a paper

questionnaire in the presence of the researcher (due to a technical problem), 69 pupils who

filled out the questionnaire on a computer without the researcher being present and 162 pupils

who filled out a paper questionnaire without the researcher being present (in both cases due to

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planning issues in the schools involved). We chose not to use the data of 41 youngsters,

because they were living in the Netherlands (n=18), because they were exchange students

(n=2) or because of incomplete information on the birthplace of their (grand)parents, which

would have been problematic given the importance of this data (n=21). Tables 1 and 2

provide demographic information.

Variables

Dependent variables

National self-identification was measured by the statement ‘I view myself as a Belgian’,

(inspired by the TIES questionnaire (Crul & Schneider, 2009)). Answers were on a five-point

Likert scale, with the options: ‘I do not agree at all’ (n=117; 7.4 per cent), ‘I do not agree’

(n=90; 5.7 per cent), ‘neutral’ (n=249; 15.7 per cent), ‘I agree’ (n=450; 28.4 per cent) and ‘I

fully agree’ (n=617; 38.9 per cent). The option ‘this does not apply to me’ was added to all of

the self-identification questions, and was intended to be used by non-migrant youngsters in

relation to the question about ethnic self-identification. However, a few of the youngsters used

it in relation to the question on national self-identification (n=40; 2.5 per cent). It is unclear

what their intention was by doing so, and we chose to treat these variables as missing.

Subnational (Flemish) self-identification was measured by using the statement ‘I view

myself as a Fleming’. Participants answered on a five-point Likert scale: ‘I do not agree at all’

(n=144; 9.2 per cent), ‘I do not agree’ (n=103; 6.6 per cent), ‘neutral’ (n=211; 13.5 per cent),

‘I agree’ (n=389; 25.0 per cent), ‘I fully agree’ (n=662; 42.5 per cent). Again, a few

youngsters chose the option ‘this does not apply to me’ (n=50; 3.2 per cent), and as in the

previous case, these variables were treated as missing.

Ethnic self-identification–referring to the country the respondent was born in and/or

their (grand)parents were born in–was measured by the statement ‘I view myself as a

Turk/Moroccan/Italian/… (whichever applies to you)’. A five-point Likert scale was again

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used: ‘I do not agree at all’ (n=97; 12.8 per cent), ‘I do not agree’ (n=31; 4.1 per cent),

‘neutral’ (n=83; 10.9 per cent), ‘I agree’ (n=156; 20.5 per cent), ‘I fully agree’ (n=269; 35.4

per cent). The option ‘this does not apply to me’ was also available here, so that youngsters

without a migration background could indicate this, but a few youngsters with a migration

background also ticked this option (n=124; 16.3 per cent). These were treated as missing.

Identification with the city level was measured by using the statement ‘I view myself

as an inhabitant of Genk/Sint-Niklaas/… (connected to your city)’. A five-point Likert scale

was again given: ‘I do not agree at all’ (n=109; 6.9 per cent), ‘I do not agree’ (n=86; 5.4 per

cent), ‘neutral’ (n=278; 17.5 per cent), ‘I agree’ (n=520; 32.8 per cent), ‘I fully agree’ (n=499;

31.4 per cent). Those who answered ‘this does not apply to me’ (n=63; 4.0 per cent) were

treated as missing.

Independent variables

The youngsters were grouped into generations on the basis of their birth country and that of

their parents and grandparents. In accordance with the definition of the Flemish Government

(Vlaams Economisch Sociaal Overlegcomité, 2014), we labelled youngsters who were not

born in Belgium as first generation (G1), those with two parents born abroad as second

generation (G2), those with one parent born abroad as generation 2.5 (G2.5), respondents with

a least one grandparent born abroad as third generation (G3) and participants without a

migration background as ‘zero generation’ (G0). On the basis of the same information, we

distinguished between youngsters with EU15, non-EU15 and Belgian origins. If the youngster

or one of his/her (grand)parents was born outside the EU15, he/she was classified into the

non-EU15 group. If the youngster or one of his/her (grandparents) was born in an EU15

country other than Belgium, he/she was classified into the EU15 group. Members of the G0

group were classified into a ‘Belgian’ category.

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For the socioeconomic status (SES) of the participant’s family, respondents gave the

title and a short description of their parents’ occupation. The occupations were then classified

into eight categories, based on the ten used in the International Standard Classification of

Occupations (ISCO-08: International Labour Organization, 2012). The category ‘skilled

agricultural, forestry and fishery workers’ contained only a few cases (n=9) and was merged

with the category ‘craft and related trades workers’. The category ‘armed forces occupations’

was not used; we chose to sort the few people in this category into the category that comes

closest to the actual job they were performing in the armed forces. Lastly, non-working

parents were placed in the category ‘elementary occupations’ (in line with Bouverne-De Bie,

Van de Walle, & Bradt, 2014). In the ISCO-08, the highest SES is represented by the lowest

number. We inverted this coding to make interpretation easier: SES now ranges from 1

(elementary occupations and the non-working) to 8 (managers). The family’s SES was based

on the occupation of the highest-scoring parent. If there was only one parent or if information

about one of the parents was missing, the information for the remaining parent was used

(n=115). If the information for both parents was missing, the family’s SES was considered as

missing (n=12).

With regard to educational track, 405 pupils were in the general track (25.6 per cent),

485 in the technical track (30.6 per cent) and 695 in the (part-time or full-time) vocational

track (43.8 per cent). In total, the proportions by gender are 961 (60.6 per cent) boys and 626

(39.4 per cent) girls.

INSERT TABLES 1 AND 2 HERE

Analysis

Several multilevel linear regression analyses with the variables national, subnational,

ethnic and city level self-identification were carried out. We used multilevel analyses to

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correct for the nesting of pupils within schools (Hox, 2010). All variable components

were fixed, as the school level was not our interest.

A first set of linear regressions was performed to examine whether there were

significant differences in the national identifications of the different generations of youngsters

(G0, G1, G2, G2.5, G3) (hypothesis one), controlling for three factors that are known to affect

identification processes: gender (Fibbi et al., 2015), SES (Marsden, 2014) and educational

track (Van Praag, Boone, Stevens, & Van Houtte, 2015). Similarly, a second set of linear

regressions was used to explore differences in the subnational identifications (hypothesis

two). To formulate a more precise answer to the question of whether the Flemish identity is

indeed less permeable for those with a migration background, we conducted a paired sample

t-test to compare the strength of Belgian and Flemish identifications for all groups (G0, G1,

G2, G2.5, G3). Using a third set of linear regressions, we examined differences in the ethnic

identifications of the youngsters with a migration background (G1, G2, G2.5, G3: hypothesis

three). A fourth set of linear regressions considered the identifications with the city for the

different groups (G0, G1, G2, G2.5, G3: hypothesis four). All these analyses were carried out

separately for the EU15 group on the one hand, and the non-EU15 group on the other, to see

whether the identifications of the youths differed according to their region of origin

(hypothesis five). None of the variables were centred.

Results

INSERT TABLES 3A AND 3B HERE

National identifications (hypotheses 1 and 5)

Models one and two (Table 4) show that compared with non-migrants, all the

youngsters with a migration background identify significantly less strongly with

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Belgium. However, there are differences between the migrant generations and between

EU15 and non-EU15 migrants. Additional multilevel regression analyses, identical to

those in models one and two but with G2.5 as the reference category (not shown)

illustrate that for the non-EU15 group, G2.5 identify significantly more strongly with

the national level than G1 and G3 (no significant difference with G2), while in the

EU15 group, there seems to be a relatively linear pathway: G3 identifies more strongly

with the national level than G2.5, who in turn identify more strongly with the national

level than G2 and G1. Within the EU15 group, those with a higher SES identify more

strongly with the national level.

INSERT TABLE 4 HERE

Subnational identifications (hypotheses 2 and 5)

Compared with non-migrant youths, all the youngsters with a migration background

identify significantly less with Flanders (Table 4, models 3 and 4). A paired sample t-

test to check significant differences between the strength of the identification with

Flanders and Belgium (indicated by asterisks in Tables 3A and 3B) shows that G0

identifies significantly more strongly with Flanders than Belgium, and that the non-

EU15 G1, G2 and G2.5 identify significantly more strongly with Belgium than with

Flanders. This means that the gap between non-migrants and those with a migration

background is larger in their regional identification than in their national identification,

especially for the non-EU15 G1, G2 and G2.5. The difference is much smaller in the

EU15 group, where G2.5 actually identifies slightly more with the Flemish level than

the Belgian. Those in the general and technical tracks identify more strongly with

Flanders than those in the vocational tracks. Within the EU15 group, those with a higher

SES identify more strongly with the Flemish level.

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Ethnic identifications (hypotheses 3 and 5)

Models five and six (Table 4) show that for the non-EU15 group, G2 identifies

significantly more with their ethnic background than G1 and G3 (with no significant

difference between G2 and G2.5). In the EU15 group, G2 identifies significantly more

with their ethnic background than G3, with no significant difference between G2 and

G1, and between G2.5 and G2. This could be considered as a ‘vanishing’ of the

importance of ethnic identifications, confirming our third hypothesis, given that the

strength of the identifications of G1, G2 and G2.5 is at a similar level, but is lower in

G3. SES and educational track do not play a role here.

City identifications (hypotheses 4 and 5)

The G1 of both EU15 and non-EU15 groups and the non-EU15 G3 identify

significantly less with the city than non-migrants, while the strength of city level

identifications in the G2 and G2.5 of both groups and the EU15 G3 do not differ

significantly from the non-migrants. With regard to the differences between generations,

once again the pattern differs between the EU15 and the non-EU15 groups. For the non-

EU15 group, additional multilevel regression analyses–identical to the ones in models

seven and eight but with G2.5 as the reference category (not shown) illustrate that G2.5

identifies more strongly with the city level than G1 and G3 (no significant difference

between G2.5 and G2). In the EU15 group, these additional multilevel regression

analyses show that the city level identification is significantly lower in G1 than G2.5,

but does not differ significantly when comparing G2.5 with G2 or G3. Given that in G2,

G2.5 and G3 we also did not find statistically significant differences from the city level

identifications of non-migrants, this could be interpreted as a rapid (already from the

second generation onwards) attainment of the same level of city level identification as

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non-migrants. Those in the general and technical educational tracks identify more

strongly with the city than those in the vocational track.

Differences between EU15 and non-EU15 (hypothesis 5)

Our last hypothesis is that there might be differences in the patterns of identification

across the generations between the EU15 and the non-EU15 group (see graphs 1 and 2).

As indicated above, G2.5 of the non-EU15 group identify significantly more with the

national, ethnic and city level than the first and third generations, while this is not the

case for the EU15 group. One could say that there is a peak in the strength of

identification in the non-EU15 2.5 generation (for the ethnic level, the peak is in G2 and

G2.5), while the differences between generations in the EU15 group could instead be

seen as linear.

Conclusion and discussion

Segmented assimilation theory?

We see some evidence for the segmented assimilation theory (Portes & Zhou, 1993),

given that the EU15 group and the non-EU15 group each seem to have a distinctive

pattern over generations. In the EU15 group, a classical assimilation pathway can be

discerned: each generation respectively identifies significantly more with the

(sub)national level; ethnic identifications do not differ significantly between the first

and the second or 2.5 generation, but decline significantly by the third; and city level

identifications are significantly lower in the first generation compared with the non-

migrants, but then climb for the second, 2.5 and third generation to a similar level as for

the non-migrants.

In the non-EU15 group, the second and 2.5 generation group have significantly

stronger ethnic identifications than the first and third generations, which might indicate

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a ‘thickening ethnic identity’. Given that they have to deal more frequently with

discrimination and othering practices (Andre & Dronkers, 2017; Meuleman & Billiet,

2003), this reflects literature on ‘reactive ethnicity’ (Portes & Rumbaut, 2001).

However, we did not find evidence that a strong ethnic identification is more common

in low-income or low-status groups (Portes & Zhou, 1993), since SES and educational

track do not play a significant role in either group.

The non-EU15 2.5 generation not only identifies more strongly than the first and

third with the ethnic level, but also with the national and city levels. The higher levels of

ethnic identification in the second and 2.5 generation, might thus be a sign of a

heightened salience of identity questions, rather than a sign of withdrawing into an

ethnic identity. Costigan and Dokis (2006), for example, document how Chinese

second-generation youngsters have a stronger ethnic identification than their parents.

These researchers hypothesize that the parents were surer of their ethnic identity, while

the youngsters had been living as an ethnic minority for a larger part of their life and

had been exploring their identities more thoroughly. Espiritu (1994) argues that stronger

ethnic identifications of second generations are often not a sign of a ‘return’ to a

particular identity, but instead a newly conceptualized identity that challenges notions of

what it means to be ‘ethnic’, from both within and outside the ethnic group. Sometimes

this newly built identity includes a political component, particularly related to fighting

racism against a particular group. Further to this, Van Kerckem shows that second and

2.5 generation youngsters with a Turkish background in Belgium ‘stress their Belgian

identity, as a way of reclaiming an identity that is denied to them from the outside’

(2016, p. 242).

However, these findings need to be interpreted cautiously and need further in-

depth research, because composition effects might have played a role. First, the non-

18

EU15 second and 2.5 generation and the non-EU15 third generation are composed

differently in terms of for example country of origin, migration history and religion.

Second, the 2.5 generation is composed of both young people with one migrant parent

and one parent without a migration background as well as young people with one

migrant parent and one second generation parent. Our data, however, do not allow to

make a finer-grained distinction within the different generations. Further research might

help to determine whether this peak in the second and 2.5 generation is indeed truly

associated with their generational status or rather with other differences.

It is worthwhile to further research this tentative finding about the specific

position of the second and 2.5 generation, given the important theoretical and policy

implications this might have. For policy and practice, two specific issues can be

informative. First, a strong identification with the country their parents were born in can

go together with a strong identification with the country they are living in. Overly

dramatic notions of ‘reactive ethnicity’ (Portes & Zhou, 1993) or ‘parallel societies’ (De

Bock, 2018) should be used carefully: it is not because a person’s roots are important

that their current country of residence is ‘rejected’. Second, it means that the sometimes

difficult position of this generation needs to be recognized, necessitating sensitivity and

actions related to the challenges they face, including adequate psychosocial support and

a proper anti-discrimination policy in different life domains such as labour and housing

(Marsden, 2014; Van Praag et al., 2015).

No Belgian/Fleming until the third generation?

Whereas there is a strengthening of (sub)national identification over generations, the

data also shows that compared with non-migrants, all the generations (both EU15 and

non-EU15) identify significantly less as Belgians and Flemings. The gap between

migrants and non-migrants is even greater for the Flemish than for the Belgian level,

19

which might be explained by the fact that the Flemish identity is often construed as

more ethnic (related to ancestry) than civic (based on ideological principles that can be

discussed) (Meeus, Duriez, Vanbeselaere, & Boen, 2010). It is quite striking that these

differences exist so strongly up to the third generation. In-depth qualitative research

could give more insight into why this is the case. In Runfors’ (2016) research, for

example, the descendants of migrants with different origins share the experience of

being situated as ‘invandrare’ (Swedish for ‘immigrants’). They describe situations in

which it becomes clear that no matter how they try to position themselves, they will

always be seen as invandrare; as people who live in Sweden but who are not Swedish,

because of their physical appearance and/or the specific sociolect they use. The young

people in Runfors’ (2016) research also use the label invandrare to describe themselves,

which she interprets as using the language of domination rather than as showing an act

of resistance. Not having a strong national identification might also be a conscious act

of resistance, because these young people might feel there is no space to identify as such

(Marsden, 2014), or because of historical reasons related to colonialism and exploitation

or current experiences of discrimination and institutional racism (Imoagene, 2012).

Is it the neighbourhood that matters?

Our results show that within the EU15 group, the second, 2.5 and third generations

identify as strongly with their city as non-migrants do. In the non-EU15 group,

however, this is only the case for the second and 2.5 generation. The city level thus

plays a role, but not as much as expected from the literature. This might be because

certain neighbourhoods within the city are highly important in the identifications of

young people with a migrant background, instead of the city as a whole. If young people

identify strongly with the neighbourhood, mostly because they feel it is the only real

option they have (Runfors, 2016), there is reason for concern. With regard to policy, this

20

would mean that not only should cities or neighbourhoods promote themselves as

diverse (Alba & Foner, 2015), but nation and nationality should be redefined as being

intrinsically diverse (Benton & Gomez, 2014).

Educational track

Those in the general and technical tracks identify more strongly with Flanders and with

the city they live in than those in a vocational track. This might be because the latter

have a lower awareness of other levels beyond the often publicly debated ethnic and

national ones, since young people in lesser appreciated educational tracks often show

less political knowledge (Hoskins, Janmaat, Han, & Muijs, 2016).

Limitations and implications for further research

The current research should be understood as an attempt to gain a broad view of the

identifications of four generations of youngsters with a migrant background. It results in

several avenues for further research.

First, there is nowadays more or less a consensus in literature that identities are

fluid, dynamic and situational (Verkuyten, 2005), while in this study, identifications

were explored by asking direct questions that probe relatively static forms of

identification. As suggested by Meinhof and Galasinski (2002), we argue that by asking

direct questions, we tap into the young people’s consciously held beliefs about their

self-identifications, acknowledging that this is only a partial view of those self-

identifications. Using different methods of elicitation to tap into more unconscious and

sometimes paradoxical beliefs and attitudes concerning self-identification, as Meinhof

and Galasinski (2002) also do, can be a fruitful way to gain wider knowledge. However,

ethnic and national identifications are often central in people’s lives and also have a

stable aspect, because they are linked to emotions and loyalties that do not change

21

readily, and because they are often embedded in supportive or oppressive social

relationships that are hard to change (Verkuyten, 2005). Although adolescence is

traditionally seen as a phase of identity development, age does not seem to influence the

achievement of ethnic identity (Verkuyten, 2005). Furthermore, we know that even very

young children are already aware of ethnic divisions within society (Connolly, 2011).

We therefore believe that our results are valuable, but should be complemented by

research that focuses more on the fluid, dynamic and situational aspects of

identification. This has been carried out using qualitative research and should be further

pursued in the future, however, the search for a quantitative method that allows more

fluidity than the one used in this study would contribute a great deal to knowledge-

building in this field.

Second, the labels that were given meant the youngsters did not have an option to

identify differently concerning ethnicity or to identify in a non-ethnic way, except for

identifications with the city level. Nevertheless, we know that ethnic and national

identifications are salient in people’s lives (Verkuyten, 2005). Vathi (2015), for example,

shows that migrants and their children often identify as being a ‘youngster’ or a ‘mother’.

Some of the youths chose the answer option of ‘does not apply to me’, especially in the

question about ethnic identities. This category was intended for non-migrant youngsters who

did not have an ethnic background other than Belgian, but a few of the youngsters with a

migration background also selected it. Though it is unclear what the respondents meant by

doing so, it might be interpreted as an indication that these youngsters had other

identifications in mind that they considered more relevant than the options we offered.

Lastly, a larger group of respondents would allow us to make finer-grained

distinctions regarding origin, migration history and religion, which could help us to better

understand the unexplained intra-generational differences (Imoagene, 2012). Composition

22

effects, resulting from aggregating young people with different backgrounds and migration

histories into one generation, could accordingly be reduced. For example, it is important to

analyse separately what happens to young people of Eastern European descent, given that

they are less ‘visibly different’ (Colic-Peisker & Tilbury, 2007).

23

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Table 1: Distribution of ethnic origin by generation

G1

n=150

G2

n=181

G2.5

n=244

G3

n=209

EU1

5

n=35

1

Netherlands

Spain

others

2

9

5

1

1

Italy

others

2

2

1

4

Italy-Belgium

Netherlands-

Belgium

Germany-Belgium

Greece-Belgium

France-Belgium

Spain-Belgium

others

3

4

2

7

1

7

1

1

8

8

5

Italy-Belgium

Netherlands-

Belgium

Italy

Germany-Belgium

France-Belgium

Spain-Belgium

others

43

30

20

19

11

11

26

Non

EU1

5

n=43

3

Morocco

Turkey

Kosovo

Russia

India

others

1

4

1

4

1

3

1

2

5

4

Turkey

Moroc

co

others

8

0

4

1

2

4

Turkey-Belgium

Morocco-Belgium

Poland-Belgium

Congo-Belgium

others

6

7

3

1

6

5

2

5

Poland-Belgium

Morocco

Turkey

others

12

6

6

25

31

7

Note: ethnic origins groups with at least n=5 were counted separately, the others were

grouped into the ‘others’ category.

32

Table 2: Distribution of gender, age, socioeconomic status, educational track and region of origin by generation

G1

(n= 150)

G2

(n= 181)

G2.5

(n=244)

G3

(n= 209)

G0

(n= 803)

Total group

(n= 1,587)

Gender

(n=1,587)

Male 108 (72.0%) 117 (64.6%) 153 (62.7%) 134 (64.1%) 449 (55.9%) 961 (60.6%)

Female 42 (28.0%) 64 (35.4%) 91 (37.3%) 75 (35.9%) 354 (44.1%) 626 (39.4%)

Age*

(n=1,587)

19.03

(1.39; 15-25)

18.49

(1.23;14-23)

18.32

(1.09; 16-22)

18.17

(1.02; 16-21)

18.04

(0.95; 14-22)

18.24

(1.10, 14-25)

Socioeconomic

status

Elementary

occupations &

non-working

52 (35.6%) 73 (40.3%) 37(15.3%) 17 (8.3%) 64 (8.0%) 243 (15.4%)

33

(n=1,575) Plant and

machine

operators, and

assemblers

17 (11.6%) 17 (9.4%) 35 (14.5%) 19 (9.3%) 53 (6.6%) 141 (9.0%)

Craft and related

trades workers &

skilled

agricultural,

forestry and

fishery workers

30 (20.5%) 33 (18.2%) 42 (17.4%) 26 (12.7%) 84 (10.5%) 215 (13.7%)

Service and sales

workers

13 (8.9%) 21 (11.6%) 29 (12.0%) 36 (17.6%) 103 (12.9%) 202 (12.8%)

Clerical support

workers

7 (4.8%) 8 (4.4%) 13 (5.4%) 17 (8.3%) 70 (8.7%) 115 (7.3%)

Technicians and 12 (8.2%) 14 (7.7%) 35 (14.5%) 33 (16.1%) 121 (15.1%) 215 (13.7%)

34

associate

professionals

Professionals 6 (4.1%) 11 (6.1%) 34 (14.0%) 32 (15.6%) 191 (23.8%) 274 (17.4%)

Managers 9 (6.2%) 4 (2.2%) 17 (7.0%) 25 (12.2%) 115 (14.4%) 170 (10.8%)

Educational

track

(n=1,585)

General 19 (12.7%) 21 (11.7%) 51 (20.9%) 43 (20.7%) 271 (33.7%) 405 (25.6%)

Technical 35 (23.3%) 54 (30.0%) 73 (29.9%) 82 (39.4%) 241 (30.0%) 485 (30.6%)

Vocational 96 (64.0%) 105 (58.3%) 120 (49.2%) 83 (39.9%) 291 (36.2%) 695 (43.8%)

Region of origin

(n=1,587)

Belgium 0 0 0 0 803 (100%) 803 (50.6%)

EU15 45 (30.0%) 36 (19.9%) 110 (45.1%) 160 (76.6%) 0 351 (22.1%)

Non-EU15 105 (70.0%) 145 (80.1%) 134 (54.9%) 49 (23.4%) 0 433 (27.3%)

* Mean (SD; range).

35

Table 3A: Distribution of identifications with the national, subnational, ethnic and city levels

by region of origin and generation for non-EU15 and Belgium

Identification National Subnational2 Ethnic City level

Generation1

G1

G2

G2.5

G3

G0

2.87 (1.36)

3.29 (1.17)

3.57 (1.21)

3.09 (1.28)

4.39 (0.90)

2.66 (1.32)*

2.92 (1.32)***

3.17 (1.43)***

3.18 (1.27)

4.49 (0.86)**

3.53 (1.53)

4.23 (1.08)

4.10 (1.24)

3.25 (1.61)

/

3.29 (1.30)

3.78 (1.06)

3.91 (1.13)

3.44 (1.03)

3.92 (1.16)

Welch’s F Welch’s F

(4;183.81)

=68.403***

G1<G2*

G1<G2.5***

G1<G0***

G2<G0***

G2.5<G0***

G3<G0***

Welch’s F

(4;176.01)

=107.98***

G1<G2.5**

G1<G0***

G2<G0***

G2.5<G0***

G3<G0***

Welch’s F

(3;126.94)

=7.7307***

G1<G2***

G3<G2***

G1<G2.5*

G3<G2.5**

Welch’s F

(4;190.99)

=7.0137***

G1<G2*

G1<G2.5***

G1<G0***

1Mean (SD).

2Significant difference from national identification?

*p<0.05; ** p<0.01; ***p<0.001.

36

Table 3B: Distribution of identifications with the national, subnational, ethnic and city levels

by region of origin and generation for EU15 and Belgium

Identification National Subnational2 Ethnic City level

Generation1

G1

G2

G2.5

G3

G0

2.26 (1.21)

2.82 (1.18)

3.58 (1.18)

3.89 (1.15)

4.39 (0.90)

2.26 (1.27)

2.79 (1.29)

3.72 (1.17)*

3.85 (1.17)

4.49 (0.86)**

3.49 (1.55)

4.22 (1.18)

3.69 (1.43)

3.11 (1.54)

/

2.98 (1.32)

3.52 (1.28)

3.78 (1.13)

4.01 (1.06)

3.92 (1.16)

Welch’s F Welch’s F

(4, 118.6)

=55.855***

G1<G2.5***

G2<G2.5**

G2<G3***

G1<G3***

G1<G0***

G2<G0***

G2.5<G0***

G3<G0***

Welch’s F

(4,176.01)

=107.98***

G1<G2.5**

G1<G0***

G2<G0***

G2.5<G0***

G3<G0***

Welch’s F

(3;99.657)

=6.6928***

G3<G2**

G3<G2.5*

Welch’s F

(4;122.36)

=6.5438***

G1<G2.5**

G1<G3***

G1<G0***

1Mean (SD).

2Significant difference from national identification?

*p<0.05; ** p<0.01; ***p<0.001.

37

Table 4: Multilevel linear regressions of generation (controlled for gender, SES, educational track) on identifications on the national, subnational,

ethnic and city levels, separately for youngsters with a migration background within the EU15 and outside of the EU15

National Subnational Ethnic City level

Non-

EU15

Model 1

EU15

Model 2

Non-

EU15

Model 3

EU15

Model 4

Non-EU15

Model 5

EU15

Model 6

Non-EU15

Model 7

EU15

Model 8

Intercept 4.31***

(0.09)

4.19***

(0.10)

4.24***

(0.10)

4.11***

(0.11)

4.25***

(0.21)

3.67***

(0.40)

3.77 ***

(0.12)

3.67***

(0.11)

Gender (ref: girls) -0.21***

(0.06)

-0.16*

(0.07)

-0.01

(0.07)

0.05

(0.07)

-0.04

(0.17)

0.13

(0.21)

-0.01

(0.08)

-0.00

(0.08)

Generation (ref: G01)

G1

G2

G2.5

-1.44***

(0.12)

-1.01 ***

(0.10)

-0.76***

-2.00***

(0.16)

-1.31***

(0.18)

-0.71***

-1.74***

(0.12)

-1.46 ***

(0.11)

-1.25***

-2.09***

(0.16)

-1.38***

(0.18)

-0.63***

-0.58**

(0.18)

/

-0.10

-0.33

(0.33)

/

-0.39

-0.58***

(0.13)

-0.06

(0.12)

0.02

-0.81***

(0.19)

-0.26

(0.21)

-0.09

38

G3

(0.10)

-1.27***

(0.15)

(0.10)

-0.42***

(0.09)

(0.10)

-1.23***

(0.16)

(0.10)

-0.54***

(0.09)

(0.17)

-0.93***

(0.25)

(0.29)

-

0.97***

(0.28)

(0.12)

-0.42*

(0.18)

(0.12)

0.11

(0.11)

SES 0.02

(0.01)

0.05***

(0.01)

0.02

(0.01)

0.03*

(0.01)

-0.03

(0.03)

-0.03

(0.04)

-0.00

(0.02)

0.02

(0.02)

Education (ref: voc)

General

Technical

0.11

(0.08)

0.13

(0.07)

0.01

(0.10)

0.02

(0.08)

0.23*

(0.10)

0.25**

(0.08)

0.22*

(0.10)

0.19*

(0.08)

-0.24

(0.24)

0.18

(0.17)

-0.06

(0.32)

0.40

(0.21)

0.34**

(0.11)

0.21*

(0.09)

0.25*

(0.11)

0.19*

(0.09)

Variance components

School

Student

8.11 e-05

1.01

0.14

0.96

0.08

1.04

0.20

0.95

0.26

1.27

0.68

1.31

0.12

1.14

0.10

1.14

B(SE); * p<0.05; ** p<0.01; *** p<0.001.

1 In models 5 and 6, G2 is the reference category.

39

40

Graph 1: Distribution of identifications with the national, subnational, ethnic and city

levels by region of origin and generation for non-EU15 and Belgium

41

Graph 2: Distribution of identifications with the national, subnational, ethnic and city

levels by region of origin and generation for EU15 and Belgium

42