Nº 2 - abril 2011

Sofia Gaspar, CIES-ISCTE-IUL, sofia.gaspar@iscte.pt


Abstract: In recent decades, the number of bi-national partnerships has been rising in most EU countries, offering an opportunity to explore new family formations in greater depth. The aim of this paper is to provide a comparative overview of EU bi-national partnership profiles in Spain and Italy. An original survey of 766 intra-EU migrants (EIMSS, 2005) who moved to these Southern countries between 1974 and 2004 has been used to identify specific attributes of cross-national unions. A Multiple Correspondence Analysis (MCA) has been employed using several variables including migration motives, age, education, occupation and the presence of children within the household. The results allowed two dimensions to be constructed which were then used to perform a K-Means Cluster Analysis. A threefold typology emerged from the analysis: Love migrant bi-national partnerships (Type 1), Eurostars’ bi-national partnerships (Type 2), and Retired bi-national partnerships (Type 3). In light of these findings, the concluding discussion evaluates the role these profiles have in researching family and migration fields and the broader EU social integration process.

Keywords: EU bi-national partnerships, EU social integration, Spain, Italy, EIMSS survey.

1. Introduction

One of the main advantages of the European Union is that it gives citizens the freedom to move from one country to another. The “Schengen Agreement” of 1985 and the Maastricht Treaty of 1992 finally established Europeans’ geographical mobility beyond national borders, making access to other EU countries and three non-EU countries[2] much easier. This European measure, in conjunction with a wave of migration in the globalized world and the rise of mass tourism, has significantly contributed to the social and cultural intermixing of different national groups. The EU’s policy measures facilitating internal migration flows, therefore, make one of the most marked contributions to the EU social integration process.

Motives that lie behind EU geographical mobility may, however, vary depending on the origin and destination country. Data from recent studies revealed that the most common reasons for intra-mobility include ‘love’ (29.2%), ‘work opportunities’ (25.2%), ‘quality of life’ (24%) and ‘study’ (7%) (Recchi, 2008; Santacreu et al, 2009) These current findings are in stark contrast with the major motivation for intra-European mobility until the 1970’s – work and economic rationales – and demonstrate that Europeans’ migration patterns have changed since that period and are now mainly structured around personal and affective rationales. These studies also present strong evidence that ‘love ties’ and family relationships are not only an effect of migratory flows, but are also an important reason for crossing national borders and going to live in a different country (Braun and Recchi, 2008). Intra-European love is therefore an important social trigger for moving; it is one of the driving forces behind individual intra-EU migration and one of the reasons for a permanent or temporary stay in a foreign culture. As such, in the coming decades, ‘love’ and ‘affection’ may be a symptom that, alongside the technocracy of EU institutions, the Europe of the people is at a private level, a rising social reality capable of building the roots of a European society ‘from below’.

The idea that EU social mobility may promote close personal contact and lead to an increase in the number of bi-national families between citizens of different countries is the starting point of this paper. Accordingly, in the next pages, the comparison between different types of cross-national partnerships in two Southern European countries, i.e. Italy and Spain, will be made. In the following section, the methodological procedures that guided the data collection, will be exposed before presenting an analysis and discussion of the three ‘ideal types’ of EU bi-national partnerships arising from the empirical analysis. In addition to summarizing the main points, the final part also reflects on the implications that these affective unions have on broader EU migration and social integration processes.

2. Data and Methodology

This article uses data from a cross-national research project – European Internal Movers Social Survey (EIMSS) -, in which intra-EU migrants were interviewed in 2004-05[3]. The target population was selected from migrants from France, Germany, Great Britain, Italy and Spain who had moved to one of the other countries between 1974 and 2003, were adults at the time of migration (18 years or older), and who were still living in their host country in 2004. The novelty of this sampling procedure was to allow a comparison of the same ethnic migrant groups in different host countries. This technique was conducted through the combination of telephone registers with linguistic information of names (i.e., probability of a name belonging to a particular nationality) in each of the five member states[4].

A questionnaire was developed and applied in the different countries, using new items as well as others the European Social Survey (ESS) and the Eurobarometer (EB) in order to provide analytical comparability between stayers and moving citizens at a later stage of research. In each country, around 250 telephone interviews were made with migrants who had come from the other four countries. The questions focused on family origin and migration history, socio-demographic information, European and national identity, quality of life, social integration, political and media behaviour and aimed to examine some major themes in the lives of EU intra-migrants[5].

However, and so as to restrict the theoretical goals set on this paper to Southern European countries, a smaller sample was taken from the overall dataset according to whether each respondent had a partner of a different nationality. In total, 766 individuals were extracted from the original sample (15.6%), representing only those respondents in a bi-national relationship and with either Italy or Spain as their place of residence[6]. This sub-sample allow a characterization to be made in this paper of the major patterns of bi-national partnerships among these two Southern European countries, providing inter-country comparability among the types of bi-national partnerships emerging from the results.

In order to examine the above mentioned research questions, a number of indicators were selected. First of all, to evaluate whether there are different profiles of bi-national family arrangements across these countries, several qualitative socio-demographic variables were chosen – migration motives, age, education, working situation and the presence of children within the household. To preserve the relational configuration of the analysis within a two-dimensional social space, a Multiple Correspondence Analysis was first performed as it is suitable to detect the topographical pattern of relationships of multiple categorical variables. After identifying the various associations between the modalities of all variables, a K-Means Cluster Analysis was then computed so as to define a typology of EU bi-national partnerships in Italy and Spain. The combination of these two techniques is a powerful multivariate instrument to simplify complexity and to draw the very specific patterns emerging from the data.

3. Results 

This section introduces the results from the empirical analysis and is divided in two main parts. First, a descriptive analysis characterises the main features of the target sample. Second, the major findings from the MCA are explained and a typology is mapped of the EU bi-national unions provided by the Cluster Analysis.

1. Descriptive Analysis

A clear portrait can be drawn from the sample from a descriptive examination of the socio-demographic features of the respondents in a bi-national marriage. Data on the geographical and national context of the relationship reveals that the number of respondents in the two countries per country of residence is quite unbalanced, with 64.5% of the respondents living in Italy and the remaining 35.5% in Spain. This suggests that there are more bi-national couples living in Italy than in Spain. If we look at the nationality variable, we observe that the French respondents have the highest proportion of bi-national unions (27.4%), followed by the English (19.6%), the Germans (18.7%), the Spanish (17.4%) and the Italians (17%). As expected, partner’s citizenship is another fundamental variable which also exhibits a somewhat unbalanced distribution: 57.2% of the respondents are married to Italian partners, 21.7% to Spanish, 8.5% to other non-EU nationalities, 6.3% to other EU partners, 2.3% to British, 2.1% to Germans, and 2% to French. If a crosstab is performed between the variables partner’s citizenship and country of residence, a somewhat different portrait emerges in each country. Among the respondents living in Italy, 88.3% have an Italian partner, 7.3% have a partner of another non-EU nationality, 1.8% a partner of another EU nationality, 1% a French partner, 1% a German partner and the remaining a British (0.2%) or a Spanish (0.4%) bi-national relationship. As for the interviewees living in Spain, 60.3% have a Spanish partner, 14.3% have a partner of another EU-nationality and 10.7% of another non-EU nationality, 6.3% have a British partner, 4% a German partner, 3.7% a French partner and only 0.7% an Italian partner ( (6)=612.918, p=0.000. V Cramer=0.895). This data is set out in Table 1:

Table 1:  Partner’s citizenship by country of residence (percentages in column)

EIMSS dataset (2005), N=766

Information related to migration motives to the country of residence shows that ‘migration to live with partner’ has the highest scores (37.5%), followed by ‘other reasons’ (which include the items ‘migration to live with family’, ‘migration for education’, ‘other reasons’ and ‘miscellaneous reasons’) (24.9%), ‘quality life migration’ (21.1%) and ‘migration for work’ (16.1%)[7]. As highlighted  by Braun and Arsene (2009:36-37), year of migration was defined according to three different migration periods – 1974 to 1983, 1984 to 1993, and 1994 to 2003. Despite the balance in migration flows over the years shown in the results, the middle period is the one with the most moves: 1974-1983 (30.9%), 1984-1993 (35.1%), and 1994-2003 (33.9%).

Some other socio-demographic features included gender and age. The gender distribution in the sample was relatively balanced although women were more represented than men (53% and 47% respectively). However, when a crosstab was performed, an unbalanced distribution between gender and residence country emerged: 62.1% of those living in Italy are women, vis-à-vis 63.6% of the males living in Spain ( (1)=46.688, p=0.000. V Cramer=0.240). This indicates that the majority of the bi-national couples living in Italy are formed by Italian men married to foreign women; on the other hand, in Spain, most of the respondents are men possibly married to Spanish females. Ages in the sample ranged between 27 and 91 years and the average was 54.4 years[8].

The educational level was structured around three levels of schooling (primary, secondary and tertiary education), and the data obtained revealed that most of the respondents hold a high educational level: 49.2% (tertiary education), 41.5% (secondary education) and 8.2% (primary education)[9]. When looking at the occupation variable, it can be observed that the vast majority of the respondents is working (65.1%), and a much smaller proportion are retired (17.5%), or either studying/doing housework/unemployed (16.1%). A crosstab analysis was performed to analyze gender differences according to occupation. As expected, the results showed that 49.9% of working respondents are men and 50.1% are women, 61.9% of retired respondents are men and 38.1% are women, and 19.5% of those who are either studying/doing housework/unemployed are men and 80.5% are women ( (4)=50.987, p=0.000. V Cramer=0.260).

Finally, the vast majority of the EU migrants in the sample have children (73%) compared to those who have not (26.5%). Again, as anticipated, a crosstab reveals that this variable is significantly associated with age. The proportion of individuals who have children increases with age: 57.7% for the 27-45 cohort, 77.2% for the 46-64 cohort, and 84.6% for those respondents older than 65 years ( (2)=40.947, p=0.000. V Cramer=0.232).

In sum, these descriptive findings are strictly related to the demographic features emerging from the initial EIMSS dataset. Most respondents with an EU bi-national relationship, belong to a highly educated social group, their move was driven primarily by ‘love reasons’, and they come from all age groups (see Braun and Arsene, 2009; Santacreu et al, 2009).

2. Multivariate Analysis 

The Multiple Correspondence Analysis (MCA) led to the selection of two main dimensions as structuring axes of the space of bi-national unions found across these two Southern European countries. Only some of the previous socio-demographic variables were selected for use in the MCA: migration motives, occupation, age, education, and children. It can be observed from Table 2 that ‘age cohorts’ and ‘occupation’ are the indicators that contribute most to structuring axis 1, while ‘migration motives’, ‘education’ and ‘children’ are the predominant indicators structuring axis 2.

Table 2. Discrimination and Contribution Values

Figures in bold indicate which dimension each variable is discriminating. * Figures below the Inertia value.

An in-depth analysis of the centroid coordinates reveals a clearly differentiating pattern between each dimension or axis. Accordingly, in dimension 1 it can be noted that the modalities ’27-45 years’, ’46-64 years’, ‘paid work’, ‘studying/housework/unemployed’ are in opposition to ‘+ 65 years’ and ‘retired’. More specifically, this opposing pattern suggests a differentiation between 1) younger individuals who are at a productive stage of their life; and 2) older persons in a non-productive stage of life. In axis 2 an opposition must be stressed between the modalities ‘’don’t have children’, tertiary education’, work migration’ ‘quality of life migration’ and ‘other reasons to migrate’ and those referring to ‘have children’, ’primary school’, ‘secondary school’ and ‘migration to live with partner’. In short, axis 2 separates 1) better educated individuals who migrate for a variety of reasons, from 2) less educated individuals who mainly migrated to live with a partner. The combined analysis of these two axes allows us to delimitate a topological configuration of EU bi-national partnerships, and to observe the specific constellations of variables underlying them. As demonstrated in Graph 1, the articulation of these two dimensions leads to the identification of some configurations in each quadrant.

In the 1st and the 2nd quadrant (upper-right portion and upper-left portion, respectively), there is a privileged association between ‘secondary school’, ‘have children’, ‘studying/housework/unemployed’, ‘migration to live with partner’ and ’46-64 years’. Another privileged association is found between modalities of the 3rd quadrant (lower-left portion), referring to ‘paid work’, ‘27-45 years’, ‘tertiary education’, ‘work migration’ and ‘don’t have children’. These categories also exhibit a close association to the category ‘other reasons to migrate’ placed on the axis 2. The last quadrant (lower-right portion) indicates a privileged association between the categories ‘quality life migration’, ‘retired’, and ‘+65 years’. These modalities are also in close proximity with the ‘primary school’ category placed on axis 1.


GRAPH 1. Topological configuration in the space of EU bi-national partnerships

Source: EIMSS dataset (2005), N=766

Having identified different topological constellations resulting from the several categories set above, a Cluster Analysis was performed using the two structuring axes of the space of bi-national partnerships defined through the MCA as reference. After this procedure, another MCA was conducted with a supplementary projection of the variable resulting from the clustering. Graph 2 displays the projection of three social types and clearly reveals the correspondence between the topological and the typological configurations of EU bi-national unions living in Italy and Spain[10].

GRAPH 2. Projection of cluster types in the space of EU bi-national partnerships

The clusters were labelled Love migrant bi-national couples (Type 1), Eurostars’ bi-national couples (Type 2) and Retired migrant bi-national couples (Type 3). Love migrant bi-national couples include 272 individuals (35.5%), and 94.9% of these respondents have children, 64.9% hold secondary education credentials, 75.7% belong to 46-64 age cohort, 64.2% are working and 72.7%  had migrated to live with their partner. Eurostars’ bi-national couples comprehend 342 respondents (44.6%), and unlike the previous type only 50.7% of them have children, 77.6% hold tertiary education degrees, 48.5% belong to the youngest age cohort (48.5%), 92.6% are working, and 32% had mentioned ‘other reasons’ as the main cause of their migration. Finally, Retired migrant bi-national partnerships include 152 European movers (19.8%), 85.9% of whom have children, 50.3% have secondary education, 88.2% have more than 65 years, 86% are retired, and 50.3% migrated to improve their quality of life.  Table 3 exhibits how the three Clusters are characterized by each of the input variables:

 Table 3:  MCA input variables by Clusters (percentages in column)

Source: EIMSS dataset (2005), N=766

After this analysis, a crosstab was performed to understand whether these three Clusters presented different configurations in relation to certain socio-geographical indicators: country of residence, nationality, and partner’s citizenship. As can be seen from Table 4, Love migrant bi-national couples predominantly live in Italy (73.9%), are mainly comprised of French (26.8%) and Spanish (26.8%) respondents, and have more Italian partners (71.1%). Eurostars’ bi-national couples are also more present in Italy (73.4%), have French (26.9%), English (22.8%) or German (21.9%) nationality, and their partners also tend to be Italians (60.5%). Lastly, Retired bi-national couples chiefly have Spain as their country of residence (72.4%), the vast majority have Italian (33.6%) or German (29.6%) nationality, and about one third have Spanish partners (31.6%). The Table below presents these results with more detail:

Table 4:  Socio-geographical variables by Clusters (percentages in column)

Source: EIMSS dataset (2005), N=766

Moreover, and in order to further understand whether each of these types of bi-national unions have a different gender configuration, a crosstab analysis was run between gender and clusters. As can be observed in the Table below, Love migrant bi-national couples are mainly composed of female respondents (66.5%), Eurostars’ bi-national couples include approximately as many male respondents (50.0%) as females (50.0%), and finally, Retired migrant bi-national couples include more men (64.5%) than women (35.5%). These results suggest that retired men tend to move more frequently to Spain, and more females than males tend to move to Italy due to love motives. In contrast, the youngest and more highly educated generations of Europeans seem to exhibit a somewhat balanced migration behaviour between genders ( (2)=39.898, p=0.000. V Cramer=0.228).

Table 5: Gender by Clusters (percentages in column)

Source: EIMSS dataset (2005), N=766

“Year of migration” is the last chief indicator to be mentioned here another crosstab was run to determine whether different periods of migration were associated to the three Clusters. The results are set out in Table 6 below:

Table 6: Year of migration by Clusters (percentages in column)

EIMSS dataset (2005), N=766

As can be seen in the Table, Love migrant bi-national couples migrated mostly in the first period (1974-1983): 40.4%, in contrast with Eurostars’ bi-national couples who moved mainly during 1994-2003 (41.5%). Additionally, Retired migrant bi-national couples moved in the last period (40.8%), although an important proportion also mentioned migration to the country of residence during 1974-1983 (33.6%).

4. Discussion

The findings emerging from the data analysis clearly suggest that the threefold typology of EU bi-national unions are linked to country-specific migration processes. This means that the destination countries diverge with regard to the reasons that lead people to migrate there – love, work, life quality or other reasons -, and that the typology drawn here can be adjusted to broader EU migration and mobility movements.

According to this idea, the first type of EU partnerships (Love migrant bi-national partnerships) can be included in what Russell King (2002) calls ‘love migration’ which mainly characterizes those European movers who migrate for love or affective reasons. The existence of a partner before or after mobility was the essential factor in an individual’s decision to settle in a foreign EU state. The fact that the partner is a native functions as a trigger in the process of integration and cultural assimilation, since it enhances the social competences and assistance needed on the path towards assimilation into a foreign society, i.e. linguistic capital, emotional and social support, and cultural savoir-faire within the host community. In our data, this group is particularly represented by a migration trend to Italy, composed of citizens moving in the first (1974-1983) and second periods (1984-1993), and holding secondary level of qualifications. Women, as we have seen, are more represented in this Cluster type, so further studies should assess whether these unions include different country-specific migration patterns or whether they are gender structured.

The second type of the defined bi-national partnerships – Eurostars bi-national partnerships – adjust to a recent social group profile of European young professionals, who normally make intra-EU moves after completing tertiary education mainly driven by the wish to pursue postgraduate studies (Gaspar, 2008; King and Ruiz-Gelices, 2003), work opportunities (Favell, 2008), alternative lifestyles or love (Gaspar, 2008). Therefore, this profile includes adult movers who are normally at earlier stages of their life cycle (20-30s), and who had met their partner before (‘love reason to migrate’) or after having moved (‘study and work reasons’). Notwithstanding the fact that Eurostars tend to migrate more to extensively multicultural and urbanized countries, they can be found in any EU country which can ensure the socioeconomic opportunities that they are in search of abroad. In this particular sample, although Italy was more popular as a country of residence for bi-national partnerships, Spain is also represented to some extent.

Finally, the socio-demographic characteristics of those respondents belonging to a Retired migrant bi-national couple (Type 3) can be integrated in a migration trend which has been on the rise particularly since the 1980’s – retirement migration –, and the principal reason for moving is the search for a higher quality of life after retirement. The rationales behind the decision to move include looking for a better climate, health reasons, lower cost of living or antipathy for the countries of origin (King et al, 1998). Spain, Italy, Portugal, Malta or Greece are the preferred destinations of a group of older citizens originating from the North and who usually migrate to the South (Braun and Arsene, 2009; Recchi and Favell, 2009; King et al, 1998; Santacreu et al, 2009; Williams et al, 2000). However, comparative studies have stressed the socio-cultural differences that structure retirement migration as a phenomenon, calling our attention to the diversity found in different European regions (Casado-Díaz et al, 2004; King et al, 1998; Williams et al, 2000).  Therefore, and despite the fact that retired bi-national couples do fit into general profile of older movers, there are some specificities characterizing this sample. First of all, Spain stands out as the main ‘retirement destination’ for a group of couples generally formed by Spanish natives and Italians or French. However, and contrary to the findings of previous research (King, 2002), the level of qualifications of these partnerships is quite high compared to that of other groups of retirees, who were found to be less qualified in different destination settlements (Casado-Díaz, 2006). Moreover, these types of partnership may ‘hide’ a broader trend of ‘traditional-return migration’ formed by Spanish-German couples. They might have met during the flows of guest workers during the 1960s and 1970s, when less qualified Spanish citizens moved to Germany driven by work opportunities, married a native citizen, and decided to return to Spain once retired (see also Casado-Díaz et al, 2004:375; Recchi and Favell, 2009:12; Warnes and Williams, 2006). Further analysis should therefore assess this phenomenon in greater depth.

5. Conclusion

Free movement within the European Union has unquestionably changed the demographic configuration of migrants in various (if not all) of its member countries. Old dichotomies like the south-north labour flows have nowadays become blurred, leaving space for new types of migration to occur. In fact, during the last decades, intra-EU geographical mobility has been strongly motivated not only by economic and life quality rationales but also by love migration. These migration movements give rise to bi-national EU couples which will definitely contribute, in the long run, to a cosmopolitan and trans-cultural Europe. EU bi-national partnerships therefore represent a new form of affective liaisons in civil society that may be playing an important role, alongside rational and instrumental political measures, in the (re)definition of the idea of Europe. If love represents one of the most powerful motivations for mobility, we should start to seriously consider and evaluate its social and political consequences on future European integration.

This paper aimed to present a typology of EU bi-national unions in two southern European countries, Spain and Italy. The analysis used a dataset of 766 EU movers resident in these countries and whose partner is of a different nationality. A threefold typology revealed the existence of different profiles of bi-national partnerships – Love migrant’ bi-national partnerships, Eurostars bi-national partnerships, Retired migrant bi-national partnerships – the main characteristics of which were found to be adjustable to broader migratory patterns in Spain and Italy. These findings require further analysis and development to glean greater understanding of the socio-cultural singularities associated to each of these types, and on the impacts that migrants’ movements have to shape new demographic profiles on the destination countries.


I am particularly grateful to Anália Torres for her continuous encouragement and critical insights into my work. I am also indebted to Rui Brites and Madalena Ramos for their valuable help during the data analysis. My special thanks go to Ettore Recchi and Óscar Santacreu for generously providing access to the EIMSS database.



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[1] This paper has been presented at the ESA RN27 Mid-Term Conference held in October 2010 at Cascais (Portugal).

[2] Norway, Iceland and Liechtenstein.

[3] This research was entitled the PIONEUR project – Pioneers of European Integration ‘from below’: Mobility and the Emergence of European Identity among National and Foreign Citizens in the EU –, and was funded by the European Commission through the Fifth Framework Programme, 2003-06.

[4] As Braun and Santacreu remark (2009), there are two main problems associated to this strategy. First, it can only include those intra-EU migrants whose telephone number is in an official telephone directory. And secondly, women migrants married to male natives might be underrepresented. This last limitation was partially mitigated by including an extra network-sampling of telephone numbers of women married to male nationals of the host country.

[5] A deeper knowledge of the contents of the project can be found in the volume edited by Ettore Recchi and Adrian Favell (2009). The main EIMSS’ questionnaire can also, be found in Appendix B of the book.

[6] The EIMSS database includes a total of 4902 European citizens. The sample under analysis in this paper was extracted from the EIMSS data by first selecting, according to the respondents’ nationality, only those individuals who mentioned having a relationship with a partner of a different nationality from their own. This procedure resulted in 5 different sample files, which were further merged into a unique dataset of 766 individuals.

[7] It is important to differentiate between ‘migration to live with partner’ and ‘migration to live with family’.  The first refers to migration to join a partner, whereas the second is related to migration to live with the family of origin (parents or other relatives) (Vd. Santacreu et al, 2009:57).

[8] This variable has been recoded into three cohorts – 27-45 years (28.1%), 46-64 years (49.5%), and + 65 years (22.5%) for subsequent use as a categorical variable in the MCA.

[9] In the original EIMSS dataset this variable includes several categories according to the five different countries. However, and in order to simplify the variable’s analytical comparability, it has been recoded into three educational stages: primary (6 years of education), secondary (12 years of education) and tertiary education (university).

[10] The solution of three groups was previously confirmed by a Hierarchical Cluster Analysis (Ward’s Method and Furthest Neighbor), after which a final definition was computed by the K-Means Cluster Analysis in order to optimize the partition into three groups (see Carvalho, 2008).

Autores: Sofia Gaspar