Appendix

Data and methods of analysis

A.1 Systematic content analysis of job advertisements in newspapers and survey on foreign language use at work (Chapter 2)

Content analysis

Our sample consists of job advertisements in four national and local newspapers from Germany (Frankfurter Allgemeine Zeitung [FAZ] and Berliner Morgenpost) and the Netherlands (de Volkskrant and Leeuwarder Courant) published in the years 1960, 1980, 2000, 2010, and 2014. The four newspapers are among the most important local and national newspapers in their respective countries. A random sample of 250 job advertisements per year was drawn from issues published in early February or March.

The job specifications were analyzed according to the method of systematic content analysis. The coding unit is the single advertised position. Thus, one job advertisement could contain one or more job offers. Postings that referred to inaccessible sources (such as the Internet) or offering directorships, trainings, apprenticeships, internships, voluntary services, and so forth were not coded. The coding scheme includes measures of the exact job title (open code), its professional field, and required qualifications as well as the required transnational human capital. We coded four different dimensions for transnational human capital. The first dimension was foreign language skills. We coded whether a job posting asked for a foreign language, and if so, which one(s). The second dimension of transnational human capital refers to knowledge about or expertise concerning other countries and societies. The third dimension of transnational human capital is defined as cross-cultural competence. It refers to the openness, understanding, and empathy required to properly communicate and interact with people from diverse cultural backgrounds. Postings that asked for such things as “ability to work in international teams” or “openness towards other cultures” received this code. International experiences constitute the fourth dimension of transnational human capital. This code was given when advertisements asked for, for example, educational experiences abroad.

Apart from transnational human capital, the coding scheme takes into account two further dimensions of the globalization of labor markets with two indicators each: first, the internationality of the employer – measured in terms of the employer’s international orientation and the geographical location of the headquarters – and of the job position in question, referring to whether a job requires foreign business contact, travel, and foreign working languages; and second, the language of the job description and of the specific job title. The complete coding scheme was used only for the German job advertisements, while the coding of the Dutch postings was limited to the required foreign language skills, which was considered the most salient dimension of transnational human capital, and the language of the job title and description.1

Eurobarometer survey

The content analysis of job advertisements is complemented by an analysis of survey data from the 2012 Eurobarometer (EB) 77.1, which includes questions about respondents’ foreign language use. The EB surveys are conducted in the form of face-to-face interviews in all EU member states (as well as in accession countries); the sample amounts, per country, to 1,000 interviewees (on average) older than 15 years. With regard to their foreign language skills, respondents in the EB 77.1 were asked, first, about which foreign languages they speak well enough to have a conversation in, and second, about the situations in which they have used these foreign languages. The interviewees could choose from a list containing several items. Our analysis refers to the use of foreign languages at work. This includes having conversations with colleagues, reading and writing emails and letters, and business travel abroad. The responses were recoded as a dummy variable, acquiring the value “1” for mentioning at least one of these items and the value “0” for mentioning none. Only the answers of currently employed, non-immigrant interviewees were considered.

A.2 Secondary analyses of children’s access to study abroad and to foreign language acquisition in daycare centers (Chapter 3)

In Chapter 3, we draw on three different data sets to analyze children’s acquisition of transnational human capital in early childhood and in secondary school. We use the German Socio-Economic Panel study (SOEP) to look at students’ likelihood to study abroad, the German AID:A youth survey to analyze language acquisition in preschool daycare centers, and the Eurobarometer (EB) survey for a comparative analysis of access to study abroad.

Educational stays abroad: German Socio-Economic Panel study (SOEP)

The Socio-Economic Panel study, provided by the German Institute for Economic Research (DIW), is a representative longitudinal study of more than 20,000 respondents in 12,000 households. The same respondents are surveyed annually, some of them since 1984. Sixteen- to 17-year-old household members are surveyed with a special youth questionnaire. Our analyses of educational stays abroad in Section 3.1 draw on these surveys of adolescents in the SOEP waves 2000 to 2013. Table A.1 gives a summary of the variables we use (for further information on the SOEP study, see Wagner et al., 2007).

Table A.1 Variables used in the analysis of students’ educational stays abroad (SOEP)

Variable Meaning Scale Mean value (standard error)
DEPENDENT VARIABLE
Stay abroad Have you ever attended school outside of Germany? 0 (no), 1 (yes) 0.07 (0.25)
PARENTAL CAPITAL
Economic capital – income Household income, weighted by household size, in 1,000 euros Metric; categorical in tables:
1 (very low – up to €805)
2 (low – up to €1,064)
3 (high – up to €1,436)
4 (very high – more than €1,436)		 1.21 (0.68)
Institutionalized cultural capital – education Mean value of parental education; in tables: one parent’s highest educational qualification Metric (0 – neither parent has a formal qualification to 7 – both parents hold a university degree) 3.57 (1.16)
Embodied cultural capital – highbrow culture Frequency of visits to cultural events (theater, concert, lecture) – mean of both parents from different years available since 2000 Metric (from 1 – never to 4 – weekly) 1.84 (0.49)
Social capital – networking Highest educational degree of three most important supporting persons outside the household, mean of both parents Metric (from 1 – no qualification to 4 – qualification for university entry) 2.97 (0.55)
CHILD’S CAPITAL
Institutionalized cultural capital – school type School type currently attended or school type from which qualification was obtained 1 (Hauptsckule or lower), 2 (Realschule), 3 (Gesamt-, Berufs-, Fachoberschule), 4 (Gymnasium) Median = 3
Mode = 4
Embodied cultural capital – foreign language grades Child’s grade in first foreign language 1 (very good), 2 (good), 3 (fair), 4 (poor/not sufficient) 2.92 (0.86)
Embodied cultural capital – highbrow culture Frequency of the following cultural activities: reading, dancing / playing theater, actively playing music – mean value Metric (from 1 – never to 4 – weekly) 2.49 (0.95)
Social capital – supporting persons Number of relatives and friends who promote child’s school and professional career – numbers from 2006/2012, if available Metric (from 0 – none to 3 – three persons) 2.10 (0.97)
PARENTAL INVESTMENTS
Private school Did the child ever attend a private school? 0 (no), 1 (yes) 0.07 (0.25)
Parents’ involvement Involvement of parents at child’s school – sum of the following activities: attendance of parent-teacher meetings, teachers’ office hours; visit to teachers; involvement as parent representatives 0 (no activity) to 4 (all activities) 1.76 (1.02)
CHILD’S INVESTMENTS
Voluntary work Voluntary involvement in school – at least one activity (class representative, school newspaper, etc.) 0 (no), 1 (yes) 0.70 (0.46)
OPPORTUNITY STRUCTURE
Region Region of residence – East or West Germany 0 (West Germany)
1 (East Germany)		 0.28 (0.45)
City/country Main area of residence until the age of 15 1 – in the country, 2 – small town, 3 – medium-sized city, 4 – large city Median: 2
Mode: 1
Organizations Regional presence of student exchange organizations (headquarters, branch offices, and staff) in a federal state, weighted by the number of high school students Metric; categorical in tables: 1 (low), 2 (medium), 3 (high) 1.08 (0.32)
FAMILY RELATIONS
Child-rearing approach – participation Mean of three items: frequency of one’s mother asking for child’s opinions (a) prior to decision-making, (b) about family affairs, (c) frequency of one’s mother giving reasons for decisions Metric (from 0 – never) and 4 (very frequently); categorical in Table 3.4: less (2 or less), medium (2 to 3.5), more (from 3.5) 2.62 (0.85)
Child-rearing approach – communication Mean of two items: (a) frequency of talking about own experiences with one’s mother, (b) frequency of one’s mother mentioning problems Metric (from 0 – never) and 4 (very frequently); categorical in Table 3.4: less (2 or less), medium (2 to 3.5), more (from 3.5) 2.54 (0.79)
Child-rearing approach – affectionate parenting Mean of four items: (a) problems are jointly solved with mother, mother shows (b) trust, (c) love, (d) appreciation Metric (from 0 – never) and 4 (very frequently); categorical in Table 3.4: less (2 or less), medium (2 to 3.5), more (from 3.5) 2.99 (0.74)
Child-rearing approach overall Mean of all nine items regarding child-rearing approach/family life (see participation, communication, affectionate parenting) Metric (from 0 – never) and 4 (very frequently); categorical in Table 3.4: less (2 or less), medium (2 to 3.5), more (from 3.5) 2.77 (0.65)
Conflicts within the family Frequency of conflicts with parents and siblings, mean Metric (from 1 – never to 5 – very frequently) 2.79 (0.75)
CONTROL VARIABLES
Sex Sex of the child 1 (male), 2 (female) 1.48 (0.50)
Survey year Year the (youth-) survey was conducted Metric (between 2000 and 2013), dummy variables used in the regression model 2006 (3.9)

Overall, more than 3,400 non-immigrant adolescents are included in our analysis. For the multivariate models, we employed two different strategies to deal with missing values (some variables were unavailable in some waves or for some respondents): for information that is related to families and parents rather than the adolescents themselves, we draw on information given by their siblings, many of whom were interviewed as well. Secondly, for each variable in our multivariate models, we created an additional dummy variable for missing values (0 = not missing; 1 = missing). These dummy variables were included in the model, which prevented cases with missing values from being excluded from the analyses.

In Chapter 3, we first report bivariate results. For each explanatory variable, tables and measures of statistical association show to what extent this variable affects educational stays abroad. Measures of association generally refer to the original metric variables, not to the categories reported in the tables. In the book chapter, the results of our multivariate analysis are reported in an illustrative figure. For readers interested in the precise results, Table A.2 shows the estimated logit coefficients and standard errors of the logistic regression model.

Note: Own calculations based on SOEP data from 2000 to 2013. N = 3,303. Pseudo-R2: 17.2%. Missing values and survey year are controlled for (effects not reported). Clustering of children within households was taken into account in the estimation of standard errors.

Language acquisition in German daycare centers: AID:A

To analyze language acquisition in preschool daycare centers (Section 3.2), we use data collected in 2009 in the context of the project Growing Up in Germany: Everyday Worlds (“Aufwachsen in Deutschland: Alltagswelten” – AID:A) by the German Youth Institute (DJI), a research institution focusing on children, young people, and families. The data are representative for the equivalent age groups in Germany. Our analyses focus on preschool children, whose parents were interviewed in the study. Table A.3 gives a summary of the variables we use. For further information on the AID:A studies, please refer to the documentation provided by the DJI (e.g., Rauschenbach and Bien, 2012).

Table A.3 Variables used in the analysis of foreign language acquisition mdaycare centers (AID:A)

Variable Meaning Scale Distribution
DEPENDENT VARIABLE
Foreign language acquisition in the daycare center Does the child participate in any activity to learn a foreign language in the daycare center? 0 (no), 1 (yes) Yes: 14.0%
PARENTAL CAPITAL
Economic capital – income Household income, weighted by household size, in 1,000 euros Metric; categorical in tables: low (up to €1.186) medium (up to €1.945) high (from €1.945) Mean value: 1.67;
standard deviation: 0.82
Institutionalized cultural capital – education Mean value of parental education (highest educational qualification) Metric (0 – neither parent has a formal qualification to 6 – both parents hold a university degree); categorical in tables: low (max. mittlere Reife) medium (max. FH-Abschluss) high (one parent with university degree) Mean value: 3.75;
standard deviation: 1.5
Embodied cultural capital – activities Parents at least sometimes visit cultural events (museum, theater) with the child 0 (no), 1 (yes) Yes: 80.8%
Embodied cultural capital – foreign language The household language is English, French, Spanish, Italian, or a combination of these 0 (no), 1 (yes) Yes: 2.3%
Social capital – supporting person There is a person willing to provide assistance in case at least one parent is in need of help 0 (no), 1 (yes) Yes: 94.9%
PARENTAL INVESTMENTS
Fees Monthly fees for the daycare center in 100 euros (including luncheon) Metric Mean value: 1.38;
standard deviation: 0.98
Activities Child attends activities outside the daycare center (e.g., music school, painting class) 0 (no), 1 (yes) Yes: 73.2%
OPPORTUNITY STRUCTURE
Size of town Town’s number of inhabitants 1 (up to 50000)
2 (up to 500000)
3 (from 500000)
Presence of bilingual daycare centers Number of bilingual daycare centers per one million inhabitants in the region Metric; categorical in tables:
low (up to 10)
medium (up to 40)
high (more than 40)		 Mean value: 13.2;
standard deviation: 22.1
CONTROL VARIABLES
Sex Sex of the child 1 (male), 2 (female) 48.6% male
Age Age of the child Metric Mean value: 3.6 years

Our dependent variable is based on the question of whether a child participates in an activity to learn a foreign language in a daycare center. Therefore, we look only at the 2,279 non-immigrant children in our survey who actually attend a day-care center. About 14 percent of them participate in a bilingual or foreign language program. Table A.3 provides a summary of the variables we use in our analysis, and Table A.4 reports the detailed results of the multivariate logistic regression model, which were shown only graphically in Section 3.2.

Eurobarometer survey

To compare how young people’s chances of attending a school or university abroad differ across Europe (Section 3.3), we use the data of the Eurobarometer (EB) 73.3 survey from 2010. In the EB, between 500 and 1,000 respondents above the age of 15 were interviewed in each of the 27 member countries of the EU (in 2010). Interview topics included their foreign language skills and stays abroad. The data are representative when suitable weights are applied.

Note: Logistic regression models. Own calculations based on AID:A data. N = 1,884. Pseudo-R2: 9.61%.

Our dependent variable is the interviewees’ response to the question of whether they had ever attended school or studied for at least half an academic year in another country. Only about 3 percent of the respondents had done so. Our analysis is limited to adults who have finished their education and who are neither immigrants nor children of immigrants – 21,199 respondents overall. Our focus is not on the respondents’ personal characteristics – although we do control for their age, gender, and education in our models – but rather on the characteristics of the countries they live in. Therefore, we will not present a table that explains the measurement of the individual-level variables, as we did with the SOEP and AID:A data. Instead, we will briefly explain the macro-level variables and their sources.

The variable “formerly socialist country” is given the value 1 (“yes”) for all respondents who live in Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Eastern Germany; and 0 (“no”) for everyone else. In addition, we include an interaction effect of this variable with respondents’ age. This interaction variable is given the value 1 for all respondents in the aforementioned countries who were born before 1980; everyone else is assigned the value 0.

To measure a country’s level of modernization, we use the well-known Human Development Index (HDI) provided by the United Nations. It combines indicators on economic development (gross national income), life expectancy, and the level

of education in a country. The HDI ranges from zero to one in theory, whereby the least developed countries currently have values between 0.3 and 0.5 and highly modernized countries have values between 0.8 and 0.95. All EU member countries are part of the latter group (with the exception of Bulgaria and Romania, which have slightly lower values). For our analyses, we use the HDI of our EB survey year, 2010. In Table 3.10 in Chapter 3, we distinguish between low (HDI < 0.82), medium (0.82 ≤ HDI < 0.88), and high (HDI ≥ 0.88) degrees of modernization.

To measure a country’s level of globalization, we use the KOF index provided by the ETH Zurich (see Dreher, 2006; Dreher et al., 2008). It combines different indicators of economic, social, and political exchange between countries and – in theory – ranges from 0 to 100, with higher values indicating a higher degree of globalization. Again, we use data from 2010. All EU countries reach values above 70. In Table 3.10 in Chapter 3, we distinguish countries with low (KOF < 82), medium (82 ≤ KOF < 88), and high (KOF ≥ 88) levels of globalization. For the regression models, we use the original metric index, of course.

Data on the average level of foreign language skills in a country were drawn from a previous analysis of a different Eurobarometer data set (Gerhards, 2012). For each country, we know how many foreign languages people speak on average, ranging from 0.19 in Ireland to more than 2.5 in Luxembourg. In Table 3.10 in Chapter 3, we distinguish countries where people speak at least one foreign language on average, and countries where this is not the case.

Our final context-level variable is a country’s population size (in millions of people), provided by Eurostat. We use a simplified variable that differentiates between small countries with populations up to five million, medium-sized countries (population up to 20 million), and large countries with populations above 20 million people.

Table A.5 reports the detailed results of our multivariate analyses (logit coefficients and standard errors).

A.3 Qualitative interviews with parents about their children’s school year abroad (Chapter 4)

Based on a qualitative study, we wanted to work out in more detail how the familial endowment with different forms of capital, the parenting styles, and everyday family life create social inequalities in acquiring transnational human capital in Chapter 4. We used qualitative interviews in order to reconstruct whether and how the issue of a school year abroad is talked about and debated in families that belong to different social classes.

Sampling strategy

Our sampling aimed at families with children at about the age of 16 or 17, which is the typical age to spend a school year abroad. We sought to identify families that would meet the following two criteria: first, the interviewees should belong to different social classes – that is, differ in their endowment with different forms of capital. As is known from previous studies, the practice of a school year abroad is the almost exclusive preserve of the upper and middle classes, while children from the lower classes – due to the associated high costs, among other things – rarely go abroad (see in more detail Chapter 3). However, in order to be able to examine how social inequalities play out in the acquisition of transnational human capital, we still aimed to increase the contrast between “higher” and “lower” class positions among our interviewees.

Second, in the sample, we sought to include both families whose children had been abroad and those where this was not the case. This has to do with the fact that the familial class position influences, but does not fully determine, the likelihood of a school year abroad. Some children do not go abroad despite favorable (familial) conditions; others, however, manage to accomplish such a stay abroad in spite of an adverse socio-structural starting point. It is especially these latter cases that are particularly interesting, because they allow us to detect through which mechanisms and compensation strategies children from families with an unfavorable resource endowment can still succeed in going abroad.

To get in contact with families from different social classes, we selected several Gymnasium schools and comprehensive schools in neighborhoods with a disparate socio-structural composition in a German city.2 Since schools are informed about which of their students spent a school year abroad, we could get into contact with the respective families. Furthermore, we also found parents via “snowball” sampling (Mason, 2002) and through distributing leaflets at schools.

We conducted 26 interviews in total between November 2011 and July 2012. In 19 of these cases, the child was spending a year abroad at that time; in the other seven cases the child had not gone abroad. Although the school year abroad is generally a rather socially selective practice, we also achieved the necessary contrast with regard to our interviewees’ class position thanks to our sampling strategy (see Table 4.1 in Chapter 4).

Data collection

Since we were interested in the families’ capital endowment, their child-rearing practices, and general educational efforts, we interviewed the parents and not their adolescent children. The data were collected in the form of semi-structured interviews. This way, it was possible to link our qualitative study closely to our theoretical premises and to the quantitative analyses. The interview guide thus captured Pierre Bourdieu’s concept of different forms of capital as well as Annette Lareau’s idea of class-specific child-rearing practices. Additionally, our questions aimed at figuring out in more detail whether the families knew about the possibility of a school year abroad at all and how this issue was dealt with within the families.

The concrete course of the interview depended upon whether the interviewees’ child had gone abroad. If the child was abroad at that moment, the interview with his or her parents began with an introductory question asking openly how the school year abroad had come about. After encouraging the parents to elaborate more on this and making further inquiries, we posed questions on aspects not mentioned so far (e.g., on the concrete costs of the stay abroad, their way of funding it, or the parental expectations linked to it). After this, we inquired more generally about the child’s previous course of education, his or her interests, and the parents’ general educational goals and their hopes or expectations for their child’s future. If the child had not gone abroad, we started with the general part of the interview guide and only later addressed the issue of a stay abroad. All interviews were concluded with a short questionnaire asking for socio-structural information on the family; additionally, we noted any impressions made in relation to the neighborhood or the apartment of the interviewees in a short observation protocol (see on this point Strauss, 1987).

Depending on the families’ wishes, the interviews took place at their home, in a café, or in an office. About a third of the interviews were conducted with both parents; otherwise we just interviewed one parent (mostly, the mother). Having gained the interviewees’ informed consent, all interviews were recorded and then entirely transcribed and anonymized. The length of the interviews ranged from 45 minutes to two and a half hours; in most cases it took between one and one and a half hours.

Data analysis

We analyzed our qualitative interview data using qualitative content analysis methods (Mason, 2002; Kuckartz, 2014). Our categories were partly derived based on our theoretical framework. This applied to the capital endowment and the parenting styles, for instance (see Chapter 4 for a detailed description). Taking the questionnaire data and the impressions from our observation protocols into account as well, we could classify all cases in terms of their capital endowment and the familial child-rearing approach (see Table A.6 and Table 4.1). Other categories were derived inductively from our interview material. One example is the category “transmission of transnational capital” (the intergenerational transfer of attitudes and mind-sets towards geographical mobility). After we gradually extended and differentiated our coding system, the whole interview material underwent a final round of coding.

The formation of categories and the coding process were followed by a two-step analysis: first, we reconstructed the different steps of the (decision-making) process leading up to the accomplishment of a school year abroad. Along the way towards such a stay abroad we identify five stages: (1) the emergence of the issue of a school year abroad within the family, (2) the reaction of parents and children to, and negotiations on, the subject, (3) putting the idea of the school year abroad into practice, (4) supporting the child during the school year abroad, and (5) issues upon return and future expectations in connection with the school year abroad. For each stage, we analyzed how the familial capital endowment, parenting style, and specific strategies either contributed to the completion of a school year abroad or worked towards ending this process.

However, the cross-sectional perspective of our processual analysis has its price, in that the view of the connections between the dimensions and above all of specific families gets lost. Hence, second, we developed a typology that represents three class-specific ways of dealing with the issue of a school year abroad (for methodological aspects on such a typological analysis see Kuckartz, 2014). In contrast to the process analysis, we somewhat widened our view here to encompass the parents’ general educational efforts and ask what importance the acquisition of transnational human capital has for them in general and the practice of the school year abroad in particular. Starting out from our coded interview material, we determined four relevant comparative dimensions for the typology: (1) the parental forms of capital, (2) the parents’ child-rearing approach, (3) the question of whether the acquisition of transnational human capital constitutes a part of the general educational strategy, and (4) the question of whether the parents or the children were the driving force in realizing the school year abroad (see Table 4.2 in Chapter 4).

By allocating the interviewed families to these four dimensions, we identified three distinct types: the “transnationally accomplished,” the “excluded,” and the “ambitious.” Each type represents a class-specific constellation of how families typically enable, or limit, their children’s opportunities to spend a school year abroad. We illustrated these three types via a “representative case interpretation” (Kuckartz, 2014, p. 116) – that is, through the description of an exemplary case. Of all the interviewed families, 11 belong to the transnationally accomplished (of which 8 have children who attended school abroad), 3 to the excluded (of which all children stayed at home), and 8 to the ambitious (all of which have children who attended school abroad). Four families could not be clearly assigned to any type for case-specific reasons, though without forming a type of their own. In one case, the interview did not yield enough information to allow classification (due to the personal circumstances of the interviewee). Another case could belong to the “trans-nationally accomplished” when looking at the family’s capital endowment; but it does not show the habitual inculcation so characteristic for this type, which prepares such children for experiences abroad. The remaining two cases oscillate between the “transnationally accomplished” and the “ambitious.” In one case, the parenting style does not conform to the family’s general capital endowment; in the other case, the child’s motivation played a far greater role in convincing the parents of the merits of a school year abroad than one should expect given their cultural capital.

A.4 Quantitative data collection and qualitative expert interviews to describe the field of providers of student exchange programs (Chapter 5)

Our analyses in Chapter 5 aimed at reconstructing the historical development and current structure of the field of organizations that specialize in placing students and organizing stays abroad and carrying them out, as well as the logic of action and the selection criteria for students utilized by the organizations acting in the field. For these purposes, we first created a dataset with information about the structural characteristics of the organizations. Second, we conducted qualitative expert interviews with employees of the organizations.

Quantitative survey of structural features of the exchange organizations

To get an overview of the field of providers of school stays abroad, we created a dataset on the characteristics of the organizations working in this field and the services they offer. These include student exchange organizations and boarding school agencies.3 For our survey, we were able to access a number of guidebooks and manuals, on the one hand (Henrix and Terbeck, 2011; Gundlach and Schill, 2012; Terbeck, 2012; Mäder, 2013). In addition, we conducted Internet research to identify organizations that were either set up after the release of these manuals and guides or that were not considered in these publications. Furthermore, Michael Weichbrodt kindly provided us with a list of the organizations he studied (Weichbrodt, 2014). In total, we were able to identify 96 organizations: 78 student exchange organizations and 18 boarding school agencies (December 2014).4 We collected more information by consulting the organizations’ own homepages and making direct requests to the organizations. Table A.7 gives an overview of the information we collected on the characteristics of the provider organizations.

The analysis of these data and expert interviews revealed that the providers in the field can be categorized – based on (a) their program structure (in particular the program objectives, charitable goals, and available options), (b) the prices for these programs, and (c) the process for selecting or placing candidates – into three segments: a “basic,” “choice,” and “premium segment” (see Chapter 5). While the boarding school agencies exclusively belong to the premium segment, the student exchange organizations differ primarily according to whether they offer different program options. Accordingly, all organizations that offer at least one select option – that is, a choice of state (within a country), region, or school – were assigned to the choice segment; organizations that have no select options in their programs were assigned to the basic segment.

Expert interviews with employees of exchange organizations

The qualitative expert interviews with employees of different organizations were mainly designed to obtain information on their views regarding developments in the field of provider organizations and on the recruitment and selection strategies and the placement processes used by the organizations (for more on the expert interview method, see Bogner et al., 2009; Meuser and Nagel, 2009; Gläser and Laudel, 2010).

Case selection and data collection

The selection of interviewees included both boarding school agencies and the providers of student exchange programs, and in the process we took care to choose providers of different sizes and organizational forms (private/nonprofit). Except for one boarding school agency, all of the organizations we asked were willing to be interviewed.

The data were collected in the form of semi-structured interviews. The interview guide comprised five thematic blocks of questions on (1) the recruitment, application, and placement process, (2) characterizing the families whom the organization mainly served, and on how they dealt with possible difficulties during preparations for the stay abroad, (3) financing and scholarship opportunities, (4) how they deal with any problems that arise during the stay abroad, and (5) the field of providers in general and the development of the branch and its organizations in recent years.

Altogether, we conducted 15 expert interviews with employees of 13 organizations from February to October 2014 – including four nonprofit and six private student exchange organizations and three boarding school agencies. The interviewees at the student exchange organizations work for the organizations as salaried employees, freelancers, or volunteers below the level of management; for the boarding school agencies, which are significantly smaller than the student exchange organizations, we had access to the managing director.5 All interviewees had multiple years of experience working in their chosen field and dealt with application, selection, and placement issues during their practical work. In general, the interviews took place on the premises of the organizations – for example, in the employee’s office or in a meeting room where the discussions with the families also take place. In the case of freelancers or volunteers, the interviews were carried out in a coffee shop or an office at the university. In addition to these 15 interviews, we conducted an additional expert interview with an employee of an organization that does not organize stays abroad, but that guides the activities of the student exchange organizations and boarding agencies and gives them advice. All interviews were recorded with the consent of the respondents; on average, the interviews lasted one and a half hours. Since after 16 interviews, we were no longer gaining new perspectives on the aspects we were interested in, we opted not to conduct any more expert interviews (Strauss, 1987).

Data analysis

The expert interviews were partly transcribed; less relevant passages were summarized in the transcribers’ own words and were not fully transcribed. In the subsequent qualitative content analysis, we followed in particular the approach described by Jochen Gläser and Grit Laudel (2010) (see also Kuckartz, 2014). In contrast to the analysis of the parent interviews (see Section A.3), the categories were identified in an almost exclusively deductive manner. Based on our theoretical considerations, the first version of a category system was created, which – like the interview guide – consisted of five main categories: (1) the recruitment and application process, (2) characterization of the applicants and their families, (3) scholarships, (4) the care process after the stay abroad has begun, and (5) branch development and self-positioning of the organization. Each of these overarching categories in turn contained numerous subcategories.

In the next step, statements with an identical meaning were summarized – this initially took place separately for each organization; they were then synthesized to compare the organizations. At the same time, we used the information provided by the interviewees to reconstruct both the chronological order of internal processes (e.g., with respect to the selection and placement processes) and field-specific developments and to identify similarities and differences between the various organizations.

Drawing on these data and the information obtained quantitatively, we arrived at the earlier described division of the field into three different segments (the basic, choice, and premium segments). Of the interviewed student exchange organizations, we could assign two to the basic segment and eight to the choice segment; the bulk of the interviews in this area point to the greater heterogeneity of this segment. The three interviewed boarding agencies belong to the premium segment.

Notes

1 More information on the codes and coding procedures can be found in our code book, which is published online. Available at: http://www.polsoz.fu-berlin.de/en/soziologie/arbeitsbereiche/makrosoziologie/projekte/proj_humankapital.html.

2 Exchange programs for students are primarily targeted at students from these school types (see Chapter 3 and Chapter 5). Our assumption that socio-structural differences within the city should also impact upon the social composition of the student body at the individual schools was confirmed (see Table 4.1 for the unequal capital endowment of the families we interviewed).

3 This did not include agencies who exclusively offer shorter stays abroad (e.g., language courses), au pair stays, “work and travel” programs or the like, and providers that place students only in boarding schools in Germany, Austria, and German-speaking Switzerland, as well as umbrella organizations and advice centers that do not themselves offer stays abroad.

4 Unfortunately, we could not fully reconstruct how certain organizations merged with others over time, changed their business model, or ceased all activities, which means that there is a certain bias in favor of still existing organizations. According to one interviewee, however, such changes over time apply to no more than ten cases.

5 Additionally, one boarding school agency allowed us to participate in one of their advisory sessions with a family in an observer capacity.

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