Social Inclusion (ISSN: 2183–2803) 2021, Volume 9, Issue 4, Pages 278–290 https://doi.org/10.17645/si.v9i4.4568 Article Structural Embeddedness in Transnational Social Fields: Personal Networks, International (Im)Mobilities, and the Migratory Capital Paradox Renáta Hosnedlová 1, *, Ignacio Fradejas‐García 2 , Miranda J. Lubbers 3 and José Luis Molina 3 1 LEREPS, University of Toulouse, Sciences Po Toulouse, France; E‐Mail: renata.hosnedlova@ut‐capitole.fr 2 School of Social Sciences, University of Iceland, Iceland; E‐Mail: ignacio@hi.is 3 Department of Social and Cultural Anthropology, Autonomous University of Barcelona, Spain; E‐Mails: mirandajessica.lubbers@uab.cat (M.J.L.), joseluis.molina@uab.cat (J.L.M.) * Corresponding author Submitted: 29 May 2021 | Accepted: 2 September 2021 | Published: 15 December 2021 Abstract In this article we focus on individuals’ structural embeddedness in transnational social fields (TSFs) and examine how this is related to patterns of international mobility. The main argument is that the structure of TSFs matters for (im)mobility trajectories, and thus all actors (migrants, non‐migrants, and returnees) need to be examined as a whole to obtain a deeper understanding of the role of social networks in processes of transnational mobility. Taking the case of Romanian migrants in Spain as a TSF connecting their place of origin (Dâmbovița in Romania) with their destination (Castelló in Spain), we analyze survey data for 303 migrants, non‐migrants, and returnees, sampled through an RDS‐like binational link‐tracing design. We then categorize types of personal network using an international mobility scale to assess the degree of struc‐ tural embeddedness in the TSF. An important contribution is the rigorous operationalization of TSF and assessment of the level of migratory capital of each individual. Our results reveal that migratory capital is not always linked positively with high mobility patterns and that its role is strongly related to the overall composition and structure of the TSF. Keywords migratory capital; mobility patterns; personal network typology; Romania; Spain; structural embeddedness; transnational social field Issue This article is part of the issue “In Good Company? Personal Relationships, Network Embeddedness, and Social Inclusion” edited by Miranda J. Lubbers (Autonomous University of Barcelona, Spain). © 2021 by the authors; licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu‐ tion 4.0 International License (CC BY). 1. Introduction the immobility of some individuals allows others to be mobile (e.g., Dahinden, 2010). Research into international mobility patterns tends to To grasp the effects of international migration in the focus on the immigrant population, ignoring other actors wider population, it is of great interest to know how who are immobile or have other forms of mobility, such immigrants, non‐migrants, and returnees to the country as tourists or mobile workers. However, some authors of origin relate to each other and how these relations stress that mobility and immobility define each other and facilitate or constrain transnational mobilities. From a should be studied together (see Glick‐Schiller & Salazar, theoretical point of view, immigrants and/or returnees 2013). For instance, mobility is often an informal liveli‐ provide new experiences and resources to non‐migrants hood strategy (cf. Fradejas‐García et al., 2021) or a house‐ (De Gourcy, 2007; Koikkalainen, 2019; Lacroix, 2014), hold strategy where some people migrate while others which influences migration decisions through facilitation stay behind to take care of children, aging parents or and normalization of the idea of migration. While many the family business (cf. Lubbers & Molina, 2021). Thus, empirical studies have shown that having contacts with Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 278 people who have migrated affects one’s probability of To answer these questions, we first address sev‐ engaging in future mobility (e.g., Herz et al., 2019), some eral conceptual and methodological challenges. Firstly, argue that these contacts can also discourage migration given the inherent complexity of today’s international (Faist, 1997; Mazzella, 2014) through a negative experi‐ mobility, we develop an international mobility scale ence or because of family obligations (e.g., if an individ‐ based on the migration‐mobility nexus that takes into an ual is the only sibling left in the country of origin, he/she account the stage in the migration process (of migrants, will have to take care of the aging parents). Contact with non‐migrants, returnees). Secondly, we use a personal migrants can also affect other aspects of non‐migrants’ network analysis approach which focuses on individuals’ lives, such as their material well‐being through finan‐ personal network structures and composition, to assess cial remittances. rigorously the level of migratory capital of each individ‐ Most of the authors refer to this prior mobility expe‐ ual. Next, we develop a meaningful typology of personal rience as “migratory capital,” a topic that has been the networks that includes ties of both family and friendship subject of numerous publications (e.g., Ryan et al., 2015). or acquaintances that can be related to different phases However, the usual idea of this concept remains very and levels of the (im)mobility process. Thirdly, by mea‐ general and abstract, its definition varying across differ‐ suring the TSF, we can observe different degrees of indi‐ ent works. Typically, this concept is defined as either viduals’ structural embeddedness in the TSF and relate the number of direct, personal ties an individual has to them to their specific mobility trajectories. people who have experience of migration (e.g., Garip, 2008; Massey & Aysa‐Lastra, 2011) or simply whether 2. Structural Embeddedness in the TSF one has such ties or not; yet such measures are usu‐ ally isolated from individuals’ personal networks, and lit‐ The focus on structural embeddedness allows one to go tle is known about the characteristics of relationships beyond inclusion criteria based on one location to assess with migrants, such as their role for the individual and the level of inclusion of different actors in transnational the strength of such ties. Furthermore, the question social structures simultaneously. The question is how of how these networks of migrants and non‐migrants to assess this structural embeddedness from a transna‐ are embedded in the wider transnational social field tional perspective. (TSF) has hardly been studied at all empirically. However, The great challenge resides in the fact that most stud‐ TSFs have been explicitly defined in network terms, as ies that implement the perspective of TSFs do not mea‐ a “set of multiple interlocking networks of social rela‐ sure or delimit precisely what lies within this field and tionships through which ideas, practices, and resources what is outside it. To assess the degree of structural are unequally exchanged, organized and transformed” embeddedness, the TSF must first be rigorously opera‐ (Levitt & Glick‐Schiller, 2004, p. 1009). tionalized. In this article, we do this with reference to This article examines how individuals’ structural the international corridor between Dâmbovița (Romania) embeddedness in the TSF (i.e., the number and pattern and Castelló (Spain), in which transnational practices of connections of individuals in the whole network across take place and cross‐border social formations emerge. borders) relates to their international mobility. On the Our definition of this corridor through the example of one hand, the concept of migratory capital allows a rela‐ personal networks is illustrated in Figure 1. More pre‐ tional approach to be adopted in studying the resources cisely, the delimitation of this TSF is based on the geo‐ available for migration. On the other hand, the study graphical residence of the contacts of Romanian migrants of migratory capital through the lens of transnational‐ from Dâmbovița in Castelló. Those contacts who reside in ism enables this type of capital to be examined not only either Castelló or Dâmbovița are part of the TSF; remain‐ among immigrants, but also among non‐migrants and ing contacts, namely those in Spain but not in Castelló, returnees, leading to a more accurate picture of the phe‐ those in Romania but not in Dâmbovița, or those in a nomenon. One of the key arguments of this article is that third country, are considered as being outside the TSF. the personal environment is interrelated with the whole Summing up, the application of social network analy‐ structure of the TSF, and thus the effect of migratory cap‐ sis to studies of international mobility and transnation‐ ital will depend on a focal individual’s (“ego”) degrees of alism sheds light on the relationships that are embed‐ structural embeddedness in the TSF. Our methodology ded in these processes and their outcomes (Bilecen & is inspired by the work of Mouw et al. (2014) in the con‐ Lubbers, 2021). Thus, we argue that, by studying the com‐ text of movement between Mexico and the USA, and our position of personal networks that make up this corri‐ data provide a unique opportunity to test this argument dor, paying special attention to migratory capital, and by in the European context. More precisely, we study the studying social network structure to measure individuals’ TSF between Dâmbovița in Romania and Castelló in Spain degree of structural embeddedness in TSFs, we can not by addressing two research questions: How is the migra‐ only refine the understanding of international mobility tion experience of network members related to individ‐ patterns, but also contribute knowledge about the clus‐ uals’ own mobility trajectories within the TSF? Do indi‐ tering of mobility levels in networks. As we shall show, viduals with greater migratory capital occupy structurally individuals who have more transnational links are not more central roles in the TSF? necessarily more central in the TSF. Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 279 TSF Romania Dâmbovița Romania Castelló Not Castelló Not Dâmbovița Figure 1. Delimitation of TSF based on the geographic residence of ego’s contacts. 3. Data and Methods graphic fieldwork in the community. The seeds were interviewed and asked to nominate three to six relatives, 3.1. Data friends and/or acquaintances (“referrals”) who might want to participate in the survey, ideally three in each The data come from the research project ORBITS location of the TSF. Subsequently, the referrals were con‐ (MINECO‐FEDER‐CSO2015–68687‐P) conducted in Spain tacted and interviewed upon their consent. Every new and Romania between 2016 and 2020. We analyze the interviewee was in turn asked to list three to six refer‐ quantitative data from one of the two TSFs we have rals. As a result, using nine seeds, a sample of 303 respon‐ researched, connecting the region of Castelló in Spain dents was reached: 147 immigrants in Castelló and 109 with Dâmbovița County in Romania. In this project we non‐migrants and 47 returnees in Dâmbovița. Among replicated part of the binational link‐tracing methodol‐ the returnees, 18 had returned from Castelló, while the ogy used by Mouw et al. (2014) in the USA and Mexico. remainder had had their migratory experience either in First, districts in Spain with more than 10% of their another Spanish region or in another country. total populations from Romania were identified. One of The sample diverts to some extent from the demo‐ them was Castelló, situated on Spain’s Mediterranean graphic structure of the population in terms of sex and coast, which has a substantial ceramics industry and is age, but unequally in each locality studied. While in the where Romanians are the immigrant group with the high‐ subsample of residents in Romania the share of females est share of employees with formal contracts (Bernat & is very close to their general distribution in the popu‐ Viruela, 2011). Moreover, this municipality is character‐ lation of Dâmbovița (53% females in the sample versus ized by “institutional completeness” (Molina et al., 2018), 51% females in the population), the average age (37) is a concept that refers to a high density of institutions lower than that of the total population of Dâmbovița related to the area of origin, such as churches, associa‐ (48). Conversely, in the Spanish subsample, women are tions, enterprises, and even a Romanian consulate. overrepresented (72% in the sample versus 53% in the Second, we identified the region of origin of migrants population), but the sample is similar to the Romanian with the support of the city council and secondary population in Castelló in terms of average age (44 ver‐ sources. About 80% of the people came from Dâmbovița sus 41 years, according to the Spanish National Statistics (Viruela, 2002), situated 78 km north‐west of Bucharest. Institute, 2017). For more detailed information about Hence, one of the aims of the ORBITS project was the sampling procedures, the sample characteristics, and to measure and analyze relations within this transna‐ the factors affecting the data collection see Hâncean tional corridor. et al. (2021). Data collection took place between November 2017 Each face‐to‐face interview consisted of several and July 2018. The target population consisted of adult parts: a core set of items for all types of respondents Romanian immigrants in Castelló resident there for (like sociodemographic attributes); questions depend‐ at least six months and returnees and non‐migrants ing on respondent status (e.g., working/life situation in in Dâmbovița who had social relationships with them. Romania, experience of migration to Spain, transnational The binational link‐tracing sampling design, which is sim‐ practices, return and post‐return experiences); and ques‐ ilar to respondent‐driven sampling (Heckathorn, 1997), tions in order to elicit and describe personal networks. started in the destination (Castelló) with a few diverse For the latter, five name generators were used to obtain informants (“seeds”) selected during preliminary ethno‐ up to forty network members, or “alters” in personal Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 280 network terminology. Figure 2 shows the proportion of having a lower weight than the first; and (c) future migra‐ alters elicited through each name generator by type tion intentions (depending on respondent status: plan to of respondent. Average network size ranges from four‐ return to Romania within a year/plan to return to Spain teen for immigrants to eighteen for non‐migrants and within a year/considering moving abroad) with the low‐ returnees, producing a mean number of sixteen alters. est weight. The reasoning behind the attribution of the The respondents also provided basic characteristics differentiated weights to the variables in the order given of each elicited alter (place of residence, sex, occupa‐ above was that migration is a set of past actions where tion, type of relation, contact duration, emotional prox‐ visits abroad admittedly entail much less complexity than imity, frequency of communication, religion). In addi‐ migration itself and that the intention to move is a plan tion, the questionnaire inquired about the existence that is not necessarily put into effect. As a result, we of up to nine randomly selected alter‐alter ties in have developed a fourteen‐point international mobility each personal network. The data were collected by scale that ranges from immobile to highly mobile respon‐ using either a paper‐based questionnaire or Egonet dents (see Figure 3 for the distribution of respondents software (http://sourceforge.net/projects/egonet). As a on this scale). More precisely, it goes from those who result, 4,834 alters were elicited by 303 respondents. never migrated, have not made visits abroad in the last After a very laborious procedure, the 303 personal net‐ two years, and had no intention to migrate abroad in works were interconnected into a network of networks the near future (0 points on the international mobility with 4,529 unique nodes (respondents and alters) and scale), to those who are migrants in Spain with previ‐ 7,876 ties (nomination ties and alter‐alter ties). This net‐ ous migration experience or returnees to Romania from work of networks thus represents a sample of the TSF Spain, with at least one visit abroad in the last two years, between Castelló and Dâmbovița. and still having the intention to move (migrate/return) again. This international mobility scale provides a more 3.2. Measures comprehensive view of human migratory mobility by tak‐ ing into account not only migration but also other forms The following measures were used in this study. of mobility. It thus enables analyses that go beyond the focus on the migrant group alone, allowing a joint analy‐ 3.2.1. International Mobility Scale sis with the non‐migrant and returnee populations. To measure international mobility patterns, we have 3.2.2. Network Typology combined three variables with different weights into a single scale: (a) the respondent’s migration experience, To develop a meaningful network typology providing ranging from the lowest to the highest score (never greater insights into the role of different types of net‐ migrated, migrant in Spain without other migration expe‐ works in international mobility patterns, we had to riences, and return migrant or migrant in Spain with decide the optimal solution for our data. When analyz‐ previous migration experiences), and having the highest ing personal networks, it must be realized that many weight; (b) the frequency of visits abroad (no visits, one of their characteristics are not independent. In light of visit, or more than one visit abroad in the past two years), this fact, the clustering methods seem to be adequate 100% 80% 60% 40% 20% 0% Migrants Non-migrants Returnees Up to 5 family members and 5 friends and acquaintances beyond the TSF Up to 5 returnees in Romania Up to 5 family members and 5 friends and acquaintances on the opposite of TSF Up to 5 local family members Up to 10 local friends and acquaintances Figure 2. The proportion of ties according to type of respondent and name generator (N = 303 respondents; 4,834 alters). Source: Lubbers and Molina (2016–2020). Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 281 Never migrated, no visits abroad in the last two years, no inten on to migrate abroad 0 40 Never migrated, no visits abroad in the last two years, inten on to migrate abroad 1 25 Never migrated, at least one visit abroad in the last two years, 2 24 no inten on to migrate abroad Never migrated, at least one visit abroad in the last two years, inten on to migrate abroad 3 20 Migrant in Spain, no visits to Romania in the last two years, no inten on to return 4 25 Migrant in Spain, no visits to Romania in the last two years, inten on to return 5 3 Migrant in Spain, one visit to Romania in the last two years, no inten on to return 6 45 Migrant in Spain, one visit to Romania in the last two years, inten on to return 7 7 Migrant in Spain, more than one visit to Romania in the last two years, no inten on to return 8 34 Migrant in Spain, more than one visit to Romania 9 13 in the last two years, inten on to return Returnee / Migrant to Spain with previous migra on experience, no visits to Romania / abroad in the last 10 24 two years, no inten on to return / migrate again Returnee, no visits abroad in the last two years, inten on to migrate again 11 9 Returnee / Migrant to Spain with previous migra on experience, at least one visit to Romania / abroad in 12 23 the last two years, no inten on to return Returnee / Migrant to Spain with previous migra on experience, at least one visit to Romania / abroad in 13 17 the last two years, inten on to return / migrate again 0 10 20 30 40 50 Figure 3. Distribution of cases on the fourteen‐point international mobility scale (N = 303). Source: Lubbers and Molina (2016–2020). for finding subsets of similar personal networks. Indeed, the distances between the data and the correspond‐ as Rong and Houser (2015) argue, studies analyzing the ing cluster centroid (arithmetic mean of all the data network environment in which individual decisions are points that belong to that cluster; see Yang et al., 2010). made (here, mobility decisions and behavior) can ben‐ We conducted this analysis using SPSS version 23 (SPSS efit from clustering methods, as it is difficult to spec‐ software, IBM Corp.), based on the following six net‐ ify a priori the complex behavioral rules that are often work characteristics: behind these patterns. Although there are a number of clustering methods, not all of them are suitable for 1. Percentage of alters with current and/previous our data. For example, the increasingly popular stochas‐ migration experience: This variable measures the tic modelling method is a very complex form of analy‐ percentage of alters who are currently migrants sis applicable mainly to sociocentric data (Vacca, 2020). or have past migration experience in any country One of the best established and simplest procedures is outside their country of origin. This measure indi‐ K‐means clustering, which in addition has proved to be cates the level of migratory capital in the respon‐ efficient for the analysis of a set of personal networks dent’s network (the higher the proportion of these (see, e.g., Lubbers et al., 2007; Vassilev et al., 2016). alters, the higher the level of migratory capital). In The basic idea behind K‐means clustering is the group‐ contrast to the majority of studies concentrating ing of objects (here, personal networks) into K prede‐ on the people’s own migration experience, we fined subgroups (here, network profiles) by minimizing address the role of the migration trajectory of Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 282 network members—a neglected issue in more meaningful understanding of the drivers of research—and explore its cumulative association international mobility. with an individual’s international mobility. One of the exceptions is a study by Herz et al. (2019, As the procedure relies on Euclidean distances, all vari‐ p. 102) revealing the positive effect of network ables were standardized. The analysis was performed on members with experience abroad on the probabil‐ 268 cases (out of 303), after removing outliers and cases ity of engaging in future mobility and claiming that with missing data on one or more network characteris‐ these members are a “ticket to move.” tics. Having examined three‐, four‐, five‐, six‐ and seven‐ 2. Percentage of the transnational family: This vari‐ cluster solutions, we selected the five‐cluster solution, able measures the percentage of alters who are which reached the lowest maximum number of itera‐ family members living in a country other than tions, was most easily interpretable and had the best pro‐ ego’s country of residence. portional distribution of cases in each cluster. These five 3. Percentage of transnational friends and acquain‐ clusters represent five personal network profiles, named tances: This variable measures the percentage of and described as follows: non‐kin alters living in a country other than ego’s country of residence. We pay special attention to 1. The “transnational friendship network, with aver‐ the type of relationship with network members, age migratory capital” (N = 51) is the type of net‐ especially transnational ones, as they are usually work with the highest proportion of transnational seen as one of the most important drivers of inter‐ friendship (28%), a low percentage of transna‐ national mobility. From a data availability stand‐ tional family ties (13.5%), an average proportion point, the analysis of family ties is predominant, of alters with migratory trajectories (59%) and rel‐ while there is still a lack of clarity concerning the atively strong local ties. role of friendship ties in the mobility patterns. 2. The “transnational network based on strong ties, In many studies, the importance of non‐family ties with average migratory capital” (N = 65) is charac‐ is stressed without actual verification. Some of the terized by strong local and transnational ties, with studies that take ties of friendship and acquain‐ a high proportion of transnational family mem‐ tance into account focus on a specific point in bers (29%), but a low proportion of transnational the migration process and analyze the intention friends (6%) and an average proportion of alters to move in the future (e.g., Epstein & Gang, 2006; with migratory experience (60%). Herz et al., 2019; Palloni et al., 2001), while com‐ 3. The “network embedded in the TSF, with high parative studies that give an account of the differ‐ migratory capital” (N = 53) is the type of network ent degrees of importance given to non‐family ties with the highest proportion of alters with migra‐ across the whole (im)mobility process are scarce. tory experience (72%), i.e., high levels of migra‐ In particular, insights into the relevance of these tory capital, where most alters live in Castelló or ties to immobility are missing. in Dâmbovița (90%), and where transnational ties 4. Percentage of alters residing in the transnational are strong and local ties are weak. social field: This variable measures the percent‐ 4. The “network extending the TSF, based on weak age of alters living in either the region of Castelló ties, with average migratory capital” (N = 40) or Dâmbovița County. It records the proportion of has the highest proportion of transnational alters in the space of interaction within the delim‐ alters (44%), but with the lowest proportion ited TSF. of alters within the TSF (one third on average 5. Average emotional proximity of transnational ties live beyond Castelló and Dâmbovița); further‐ with ego: This variable records the mean emo‐ more, the transnational alters are of weak emo‐ tional closeness between ego and his or her alters tional proximity. (family and/or friends/others) living in a country 5. The “local network with low migratory capital” other than ego’s country of residence. The emo‐ (N = 59) consists of a relatively low proportion of tional closeness of each alter was assessed by alters with current or previous migration experi‐ means of the following question: How close do you ence (only 24%), a very low proportion of transna‐ feel toward this person (emotionally)? The scale tional alters (7.5% of transnational family mem‐ ranges from 1 (not close at all) to 4 (very close). bers and 7% of transnational friendship ties) and 6. Average emotional proximity of local ties with ego: a relatively low proportion of alters within the TSF, Like the previous variable, this variable measures but strong relationships with local alters. the mean emotional closeness of ego to his or her local ties (i.e., having the same country of resi‐ 3.2.3. Structural Measures dence as ego). According to previous studies (e.g., Hosnedlová, 2014), comparing the quality of one’s To analyze the structural embeddedness of an individual personal environment in the local situation with in the TSF, two centrality measures that indicate the level the transnational situation can help one achieve a of intermediation capacity have been used, calculated Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 283 using UCINET 6 for Windows (Borgatti et al., 2002): ego with low migratory capital have the smallest mean mobil‐ betweenness and brokerage: ity ranking (85.33). To evaluate how individuals with dif‐ ferent network profiles differed from one another in 1. Ego betweenness is calculated as the sum of the their degree of mobility, we ran the Mann‐Whitney U proportion of times ego lies on the shortest path post hoc test. The post hoc tests indicate that the cen‐ between each pair of alters. If two alters are tral tendency of the international mobility scores of indi‐ connected, the contribution to the ego between‐ viduals with the profile “local network with low migra‐ ness of this pair is 0. If two alters are con‐ tory capital” differs significantly from all other network nected only through ego, then the contribution is 1 profiles. The profile situated on the other side of the (Hanneman & Riddle, 2005). scale (“network embedded in the TSF, with high migra‐ 2. Brokerage is calculated as the number of times ego tory capital”) also differs significantly from the other pro‐ lies on the shortest path between two other actors files, except for the profile of the “transnational friend‐ in the TSF. ship network, with average migratory capital.” Figure 4 shows the comparison of all pairs of network profiles. The rationale for using ego betweenness and brokerage These findings can be interpreted as follows. First, (from UCINET) in the analysis is that the former allows the amount of migratory capital is associated with dif‐ ego’s level of embeddedness among his or her alters in ferent levels of transnational mobility. More precisely, the TSF to be measured, while in the latter it is ego’s the higher the mobility score, the greater the presence capacity to mediate between different regions of the of migratory capital. This is shown in Figure 5, where whole network that is of interest. This latter capacity the network profiles are ordered by mean international is related to our theoretical goal of having to take into mobility, from the highest to the lowest. Second, the account not just mobility but also those who are immo‐ results also suggest which type of ties play an impor‐ bile and less connected to the TSF. tant role in international mobility. Comparing the aver‐ age international mobility of the three network profiles 4. Results with similar levels of migratory capital (those with aver‐ age migratory capital), it seems that friendship ties are a 4.1. Migratory Capital and Individual Mobility pull factor in networks that are less constrained by fam‐ ily obligations. However, we also observe that people To discover if there is a relationship between the interna‐ with lower mobility have more ties outside the TSF. More tional mobility scale and the profile of the personal net‐ precisely, individuals with “transnational friendship net‐ work, we ran a non‐parametric test, the Kruskal‐Wallis works, with average migratory capital” scored on aver‐ test, since the “international mobility scale” does not age 6.8 points on the fourteen‐point scale, compared have a normal distribution (see Figure 3). The result to 5.4 points on average for the individuals with a “net‐ shows a statistically significant association between work extending the TSF, based on weak ties, and with these two variables (H(4) = 38.3, p < 0.001), with the average migratory capital,” a difference that is statisti‐ largest mean ranking on the international mobility scale cally significant. This is an interesting observation that (167.96) for individuals with the profile of belonging to a deserves more attention. We explore this through a com‐ network embedded in the TSF, with high migratory capi‐ parative analysis of two extreme cases (those of Gabriel tal. Conversely, individuals belonging to a local network and Iulian—pseudonyms). Network profile / Mean rank of network profile (1) (2) (3) (4) (5) (1) Network embedded in the TSF, with high migratory capital / 167.96 (2) Transna onal friendship network, with average migratory capital / 154.76 (3) Transna onal network based on strong es, with average migratory capital / 140.73 (4) Network extending the TSF, based on weak es, and with average migratory capital / 126.73 (5) Local network with low migratory capital / 85.33 Legend: A sta s cally significant difference No sta s cally significant difference Figure 4. Network profiles ordered by mean rank and the results of comparison between network profiles using the Mann‐ Whitney U test (p < .05). Source: Lubbers and Molina (2016–2020). Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 284 Network embedded in the TSF, with high 7.3 migratory capital Transna onal friendship network, with 6.8 average migratory capital Transna onal network based on strong es, 6.1 with average migratory cap al Network extending the TSF, based on weak 5.4 es, and with average migratory cap al Local network with low migratory capital 3.3 0 1 2 3 4 5 6 7 8 Figure 5. Average international mobility by network profile. Source: Lubbers and Molina (2016–2020). 4.2. Two Cases transnational friendships, all living in the TSF, account for 21%. Communication with most of his transnational We have selected two cases that are very similar in terms friends is infrequent. In contrast, he communicates very of their sociodemographic characteristics (sex, level of often with his family members (parents and spouse) and education, type of employment, family situation), for the friends in Castelló. All the contacts in his network are sake of comparability. Although one is younger (Gabriel of long duration (more than ten years), and all except is 29 years old, Iulian 46), both men are of working age, one are of Romanian origin. That means that, when he both are married with children, and all the members migrated to Spain the second time, he was immersed in of their immediate families are local. Both have com‐ the local “same‐origin” network he had created in the pleted secondary vocational school, and both are work‐ past, his anchorage in the local host community being ing as drivers. However, their international mobility pat‐ almost non‐existent. terns are very different. Gabriel lives in Castelló and has As for Iulian, he has the type of network that extends a highly mobile trajectory (with the highest score on the TSF, being based on weak ties and average migratory our international mobility scale). His migration history capital. Compared to Gabriel, his network is relatively started in 2000 when he was nine years old and moved small (thirteen ties). While his local ties consist of the from Romania to Castelló to join his parents. Later, he immediate family (one parent, one sibling, and one child) married a Romanian woman, and the couple returned and four friendships of both short and long duration, to Romania. They had their first child, but things did his transnational ties consist of three friends from his not go very well there, and they decided to leave for childhood who now live in Castelló and three extended Austria. After four years of residence there, they decided family members who live outside the TSF, in England to leave, unable to adjust to Austria’s climate or society. and Italy. While he has daily communication with local They returned to Romania, and in the summer of the members, his communication with his transnational con‐ same year moved to the same place in Spain where they tacts is very scarce (lower than Gabriel’s). It seems that had lived before. When the interview took place, the cou‐ Gabriel’s transnational network, being based on strong ple had three children and were still considering return‐ ties with frequent communication, allows and sustains ing to Romania in the future. This case is a clear example this mobility. Conversely, the network with an important of multiple migrations. By contrast, Iulian can be consid‐ proportion of its contacts outside the TSF, which is not ered immobile. He scored 0 points on the international activated very often, as in Iulian’s case, does not seem to mobility scale. He has never been to Spain and has never encourage international mobility. Thus, we could hypoth‐ considered moving abroad, even though several of his esize that international mobility is reduced because the network members have lived in Italy, the UK, Belgium, individual is less embedded in the TSF and is therefore and Castelló. less exposed to the flows and circulation of knowledge, Also, they have different personal network profiles. ideas, material, monetary resources, etc., and thus to the Gabriel’s network is transnational, based on strong ties, possibilities or opportunities for moving. and with average migratory capital. His network has 29 Before affirming or rejecting this explanation, it is ties. His transnational kinship accounts for 34% out of 29 necessary to explore in more detail the characteristics of ties and is represented by his distant relatives (in‐laws the links with alters who reside outside the TSF. It should and cousins) living in Romania and some in Austria. He be remembered that there are two types of ties concern‐ considered all these network members emotionally very ing the geographical location. The first type is located in close and maintained contact on an almost daily basis a third country (that is, in a country other than Romania with those living in Austria while communicating two or Spain); the second type is located in Romania or or three times a year with those living in Romania. His Spain, but in a region other than Castelló or Dâmbovița. Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 285 We could hypothesize that most individuals who are less work profile and two structural measures introduced pre‐ mobile and have a high proportion of ties outside the TSF viously: brokerage and ego betweenness. The Kruskal‐ are non‐migrants or returnees who have most of these Wallis test indicates a statistically significant associa‐ contacts in other regions in Romania. This would point tion between the network profile and the two struc‐ to the explanation that these people have highly devel‐ tural measures: H(4) = 54.19, p < 0.001 (brokerage) and oped local social capital, and consequently, if they opt for H(4) = 17.11, p = 0.002 (ego betweenness; see the mean mobility, this mobility would be more internal than inter‐ rank for each measure of each network profile in Table 2). national. However, our data do not support this argu‐ Consequently, the structural position the respondent ment, since in this type of network most contacts outside occupies in the transnational social field is correlated with the TSF live in third countries (71% in contrast to 58% the composition of his or her personal network, which is for the rest of the networks; see Table 1). Being based in in turn associated with different mobility patterns. third countries, they are a potential source of migratory More precisely, respondents belonging to networks capital for ego, but evidently, they do not always have a with a high proportion of alters within the TSF (green positive effect on international mobility. nodes in Figure 6, type 3) and a great number of alters This encourages the argument that immobile or less with migration experience have low levels of structural mobile individuals play a special role in the dynamics embeddedness on both measures, and they are also the of the TSF and recalls the phenomenon of the interde‐ most mobile ones. This indicates that migratory capital pendence between mobility and immobility that Glick‐ is associated with high mobility patterns. Besides, the Schiller and Salazar (2013) claimed to exist. In the fol‐ low levels of structural embeddedness of highly mobile lowing analyses, we therefore go beyond the individual’s egos can be explained by the brokerage role of immo‐ direct personal ties to address his or her structural posi‐ bile egos who bridge mobile individuals between them. tion in the TSF and to enhance this dichotomy between According to social network theories, and more precisely mobility and immobility as mutually constructed poles. the work of Gould and Fernandez (1989), brokerage is described as the role played by a social actor who medi‐ 4.3. Structural Embeddedness and Migratory Capital ates contact between two alters in different contexts and in different localities. This is the case for Gabriel, for Figure 6 represents the 303 personal networks of the example, who is in direct contact with the respondents sampled individuals. The colored dots are the egos of varied networks, as well as with an important num‐ (respondents), the colors indicating the network pro‐ ber of people whose networks are local with low migra‐ files. The white dots are the egos’ contacts (alters), and tory capital (see the red nodes in Figure 6) and who are the lines between the dots represent the connections the least mobile. Conversely, those respondents with net‐ between the actors (ego‐alter or alter‐alter). Visually, we works with more contacts outside the TSF (see the violet detect some clustering on the right hand of the graph, nodes in Figure 6, type 4, like Iulian) have high degrees of marked by the red nodes, but most people with similar structural embeddedness on both measures and are the network profiles are scattered over the network. second least mobile ones. To interpret this figure, we performed a non‐ The high mean rank of both structural measures parametric analysis of the association between the net‐ means that the respondent is mostly in relationships Table 1. Differentiated means of ties by network profile (N = 268). Mean propor on o!ies Mean propor on o!ies Mean number outside the TSF in a third outside the TSF in a third o!ies outside country with respec"o country with respec"o Network profile the TSF all es beyond the TSF the network size Network embedded in the TSF, with high 1.3 60% 7% migratory capital Transna onal friendship network, with average 3.8 65% 12% migratory capital Transna onal network based on strong es, 2.3 59% 10% with average migratory capital Network extending the TSF, based on weak es, 6.8 71% 23% and with average migratory capital Local network with low migratory capital 3.8 39% 8% Total 3.4 58% 11% Source: Lubbers and Molina (2016–2020). Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 286 Legend: Network extending the TSF, based on weak es, and with average migratory capital Transna onal friendship network, with average migratory capital Local network with low Iulian Gabriel migratory capital Transna onal network based on strong es, with average migratory capital Network embedded in the TSF, with high migratory capital Figure 6. Visualization of the network of networks: example of TSF Castelló‐Dâmbovița. Source: Lubbers and Molina (2016–2020). Notes: The egos are highlighted by their network profiles; the network profiles in the legend are ordered by the mean rank of structural measures from higher to lower. Visualization made in Visone, version 2.17 (Brandes & Wagner, 2017). with people with the same or very similar network char‐ out that some family members remain immobile, as they acteristics (see the violet and red nodes around Iulian in must take care of aging parents or assume responsibil‐ Figure 6), that is, with people in the two types of net‐ ity for the continuity of the family business (see, e.g., works with the lowest scores on the international mobil‐ Dahinden, 2010). Nonetheless, the reason could also lie ity scale. Hence, it could be presumed that this respon‐ in the lack of motivation or comfort due to the inflow dent is less exposed to migratory capital than the others, of remittances in money or kind. Interestingly, our data who are connected directly to individuals with more var‐ reveal that those with a “network extending the TSF, ied networks. Also, it can be expected that these immo‐ based on weak ties, and with average migratory capi‐ bile or less mobile respondents who have a high capac‐ tal” are most likely to receive remittances from the fam‐ ity for intermediation are more constrained in terms of ily abroad. In reverse, those with “networks embedded international mobility and could exercise a special role in in the TSF, with high migratory capital,” are less likely the TSF. For instance, some scholars have already pointed to receive financial remittances but most likely to send Table 2. Mean ranks for different network profiles on two structural measures. Brokerage Ego Betweenness Network profile N (Mean rank) (Mean rank) Network extending the TSF, based on weak ties, and with average 40 177.85 160.46 migratory capital Transnational friendship network, with average migratory capital 49 172.10 150.38 Local network with low migratory capital 59 140.88 140.25 Transnational network based on strong ties, with average migratory capital 65 105.71 117.98 Network embedded in the TSF, with high migratory capital 52 86.83 106.05 Total 265 Source: Lubbers and Molina (2016–2020). Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 287 them. However, going into more detail about these roles approach unbiased samples (Heckathorn, 1997), employ‐ and reasons for (im)mobility would imply a more qualita‐ ing a variant of link‐tracing network sampling. Hence, tive approach that goes beyond the remit of this article. much more research is necessary in this field, repli‐ cating the same study to compare and advance the 5. Conclusion obtained results. In this article, quantitative methods have been pri‐ In this article, we have examined the relationship oritized. However, triangulation with more qualitative between the international mobility patterns and per‐ empirical material could help to shed more light on the sonal networks of Romanian migrants, non‐migrants functionality of different ties to improve understanding and returnees within a TSF linking Spain and Romania. of the role of network members who reside outside the We have emphasized the proportion of migratory cap‐ migration corridor or TSF in mobility processes. Also, ital in personal networks and its association with future research could examine the influence of the previ‐ (im)mobility. Moreover, we have explored their interde‐ ous and current migration experiences of network mem‐ pendencies with the degree of structural embeddedness bers separately. As underlined by Herz et al. (2019), very in the TSF. little is known about the role of returnees in future mobil‐ Our results reveal the positive association between ity. In addition, it could be helpful to consider the time of migratory capital and individuals’ international mobility, migration experience of network members. but they also show that a connection to a non‐mobile core is also necessary. This confirms the argument of Acknowledgments earlier studies that mobility and immobility are comple‐ mentary, as one cannot exist without the other (e.g., The authors wish to thank two anonymous review‐ Glick‐Schiller & Salazar, 2013). In addition, we have seen ers and the academic editor for their valuable com‐ that immobility and mobility are strongly related to ments. We also gratefully acknowledge the Romanian the composition of personal networks, which in turn team led by Marian‐Gabriel Hâncean—Iulian Oană, is linked to the structure of the TSF. Thus, not only is Bianca‐Elena Mihăilă, and Alexandra‐Adelina Stoica—for the immediate social environment important for mobil‐ their valuable contribution to this article (execution of ity patterns, so is how this immediate social environment the project, data collection, preparation, and process‐ is embedded in the wider social setting. Thus, an assess‐ ing). The authors also wish to thank Angelina Kussy ment of the overall composition of TSFs and the individ‐ and Adriana Suiu for collecting the data in Castelló. ual’s degree of structural embeddedness in it are neces‐ This article was supported by the research project sary for a better understanding of international mobility ORBITS “The Role of Social Transnational Fields in and immobility. the Emergence, Maintenance and Decay of Ethnic and Another important finding is that not all conveyors Demographic Enclaves.” This research was funded by the of migratory capital are positively associated with inter‐ Ministry of Economy, Industry and Competitiveness— national mobility patterns. Although a more profound Government of Spain (MINECO‐FEDER; CSO2015–68687‐ analysis is needed of this aspect, our data suggest that P, 2016–2020, and FPI grant number BES‐2016–076859). the effect of migratory capital on mobility depends on The third author is grateful for the support of the ICREA the type of ego’s structural embeddedness in the TSF. Foundation (Acadèmia grant). More specifically, a high proportion of ties of kinship and friendship outside a TSF are negatively correlated with Conflict of Interests international mobility. Furthermore, those respondents with a high proportion of ties outside the TSF (of whom The authors declare no conflict of interests. the majority reside in a third country, being migrants themselves) have the highest levels of ego betweenness References and brokerage scores, and are the second least mobile. Together, these associations suggest that mobile people Bernat, J. S., & Viruela, R. (2011). The economic crisis create and maintain ties that are mostly local to their and immigration: Romanian citizens in the ceramic places of origin and residence, which may be structurally tile district of Castelló (Spain). Journal of Urban and more redundant. These outcomes represent what we call Regional Analysis, 3(1), 45–65. the “paradox of migratory capital,” calling into question Bilecen, B., & Lubbers, M. L. (2021). The networked char‐ some studies whose analyses assume that having con‐ acter of migration and transnationalism. Global Net‐ tacts abroad always has a positive impact on interna‐ works. Advance online publication. https://doi.org/ tional mobility. 10.1111/glob.12317 It should not be forgotten that TSFs are “hard‐to‐ Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). count” or “hard‐to‐find” populations for which there is UCINET for Windows: Software for social network no sampling frame. This hinders assessment of the sam‐ analysis [Computer software]. Analytic Technologies. ple’s representativeness. For this reason, we have used Brandes, U., & Wagner, D. (2017). Visone (Version 2.17) respondent‐driven sampling methodologies, which can [Computer software]. University of Konstanz. Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 288 Dahinden, J. (2010). The dynamics of migrants’ transna‐ Lacroix, T. (2014). Conceptualizing transnational engage‐ tional formations: Between mobility and locality. In ments: A structure and agency perspective on R. Bauböck & T. Faist (Eds.), Transnationalism and (hometown) transnationalism. International Migra‐ diaspora. concept, theories and methods (pp. 51–71). tion Review, 48(3), 643–679. Amsterdam University Press. Levitt, P., & Glick‐Schiller, N. (2004). Conceptualizing De Gourcy, C. (2007). Le retour au prisme de ses simultaneity: A transnational social field perspec‐ détours ou comment réintroduire de la proximité tive on society. International Migration Review, 38, dans l’éloignement [The return through the prism 1002–1039. of its detours or how to reintroduce proximity Lubbers, M. J., & Molina, J. L. (2016–2020). ORBITS sur‐ into remoteness]. Revue Européenne des Migrations vey [Unpublished raw data]. Autonomous University Internationales, 23(2), 159–171. of Barcelona. Epstein, G. S., & Gang, I. N. (2006). The influence of oth‐ Lubbers, M. J., & Molina, J. L. (2021). Personal networks ers on migration plans. Review of Development Eco‐ and migration trajectories. In M. Small, B. L. Perry, nomics, 10(4), 652–665. B. Pescosolido, & N. Smith (Eds.), Personal networks: Faist, T. (1997). The crucial meso‐level. In T. Hammar, G. Classic readings and new directions in egocentric Brochmann, K. Tamas, & T. Faist (Eds.), International analysis (pp. 675–695). Cambridge University Press. migration, immobility and development: Multidisci‐ Lubbers, M. L., Molina, J. L., & McCarty, C. (2007). Per‐ plinary perspectives (pp. 187–217). Berg. sonal networks and ethnic identifications: The case Fradejas‐García, I., Molina, J. L., & Lubbers, M. J. (2021). of migrants in Spain. International Sociology, 22(6), (Im)mobilities and informality as livelihood strate‐ 721–741. gies in transnational social fields. In A. Polese (Ed.), Massey, D., & Aysa‐Lastra, M. (2011). Social capital and Migration, labour mobility and precariousness. Why international migration from Latin America. Interna‐ informality ends up replacing and supplementing the tional Journal of Population Research. https://doi. state for the invisible and the vulnerable (pp. 18–35). org/10.1155/2011/834145 Palgrave. Mazzella, S. (2014). Sociologie des migrations [Sociology Garip, F. (2008). Social capital and migration: How do sim‐ of migrations]. Presses Universitaires de France. ilar resources lead to divergent outcomes? Demogra‐ Molina, J. L., Martínez‐Cháfer, L., Molina‐Morales, F. X., & phy, 45(3), 591–617. Lubbers, M. L. (2018). Industrial districts and migrant Glick‐Schiller, N., & Salazar, N. B. (2013). Regimes of enclaves: A model of interaction. European Planning mobility across the globe. Journal of Ethnic and Studies, 26(6), 1160–1180. Migration Studies, 39(2), 183–200. Mouw, T., Chavez, S., Edelblute, H., & Verdery, A. (2014). Gould, R. V., & Fernandez, R. M. (1989). Structures Binational social networks and assimilation: A test of of mediation: A formal approach to brokerage in the importance of transnationalism. Social Problems, transaction networks. Sociological Methodology, 19, 61(3), 329–359. 89–126. Palloni, A., Massey, D. S., Ceballos, M., Espinosa, K., Hâncean, M.‐G., Lubbers, M. J., & Molina, J. L. (2021). & Spittel, M. (2001). Social capital and interna‐ Measuring transnational social fields through bina‐ tional migration: A test using information on fam‐ tional link‐tracing sampling. Plos One, 16(6). https:// ily networks. American Journal of Sociology, 106, doi.org/10.1371/journal.pone.0253042 1262–1298. Hanneman, R., & Riddle, M. (2005). Introduction to social Rong, R., & Houser, D. (2015). Exploring network behav‐ network methods. University of California Press. ior using cluster analysis. In E. Ullberg (Ed.), New Heckathorn, D. D. (1997). Respondent‐driven sampling: perspectives on internationalization and competitive‐ A new approach to the study of hidden populations. ness (pp. 161–182). Springer. Social Problems, 44(2), 174–99. Ryan, L., Erel, U., & D’Angelo, A. (2015). Migrant capital: Herz, A., Díaz‐Chorne, L., Díaz‐Catalán, C., Altissimo, A., Networks, identities and strategies, migration, dias‐ & Samuk, S. (2019). Are you mobile, too? The role poras and citizenship. Palgrave Macmillan. played by social networks in the intention to move Spanish National Statistics Institute. (2017). Continuous abroad among youth in Europe. Migration Letters, register statistics. Statistics as of 1 January 2017. 16(1), 93–104. https://www.ine.es Hosnedlová, R. (2014). Bridging the intention‐realization Vacca, R. (2020). Structure in personal networks: Con‐ gap in the process of returning. The role of networks structing and comparing typologies. Network Sci‐ in the experience of Ukrainians in Madrid [Doctoral ence, 8(2), 142–167. thesis, University Complutense of Madrid]. Univer‐ Vassilev, I., Rogers, A., Kennedy, A., Wensing, M., Koet‐ sity Complutense of Madrid. https://eprints.ucm.es/ senruijter, J., Orlando, R., Portillo, M. C., & Culliford, id/eprint/28599/1/T35813.pdf D. (2016). Social network type and long‐term condi‐ Koikkalainen, S. (2019). Nordic ties and British lives? tion management support: A cross‐sectional study Migrant capital and the case of Nordic migrants liv‐ in six European countries. Plos One, 11(8). https:// ing in London. Social Inclusion, 7(4), 171–180. dx.doi.org/10.1371%2Fjournal.pone.0161027 Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 289 Viruela, R. (2002). La nueva corriente inmigratoria de analysis purpose. In 2010 6th International Confer‐ Europa del este [The new flow of immigration ence on Advanced Information Management and Ser‐ from eastern Europe]. Cuadernos de Geografía, 72, vice (IMS) (pp. 223–228). IEEE. https://ieeexplore. 231–258. ieee.org/stamp/stamp.jsp?tp=&arnumber=5713452 Yang, X., Wang, Y., Wu, D., & Ma, A. (2010). K‐means &isnumber=5713408 based clustering on mobile usage for social network About the Authors Renáta Hosnedlová received her PhD in sociology from the Complutense University in Madrid. She is currently teaching statistics and quantitative methods in the social sciences at the University Sciences Po Toulouse and is a researcher and member of the Studies and Research Laboratory in Economics, Policies and Social Systems (LEREPS). Her work focuses on the quantitative and qualitative analysis of personal networks applied to east‐west European migration, migration decision‐making, ageing, and relational inequalities. Ignacio Fradejas‐García is a post‐doctoral researcher in the School of Social Sciences at the University of Iceland. With extensive fieldwork experience in The Gambia, Chile, Morocco, Haiti, RD Congo, Turkey, Romania, and Spain, he does research in migration, transnationalism, (im)mobilities, humanitarianism, and informality. Miranda J. Lubbers is an associate professor in social and cultural anthropology at the Autonomous University of Barcelona, Spain, and the director of the Research Group of Fundamentally Oriented Anthropology (GRAFO). In 2021, she was granted a European Research Council Advanced Grant and the ICREA Acadèmia Award. Her research addresses social cohesion and social inclusion. In particular, she analyzes the role that formal and informal social relationships and settings have in the production, mitigation, or exacerbation of exclusion and segregation. José Luis Molina is professor in the Department of Social and Cultural Anthropology (UAB). He is an economic anthropologist interested in livelihood practices in a globalized, digitalized, and unequal world. His approach is to use mixed methods with an emphasis on ethnography and personal network analysis. Southeast Europe and Romania are his particular geographical areas of interest. Social Inclusion, 2021, Volume 9, Issue 4, Pages 278–290 290