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Surveying Migrants in Europe. Experiences of the Swiss Migration-Mobility Survey

  • Ilka SteinerEmail author
  • Aljoscha Landös
Open Access
Chapter
Part of the IMISCOE Research Series book series (IMIS)

Abstract

The increasing need for information on immigrants can scarcely be satisfied by traditional population surveys or censuses. Only migrant-specific surveys allow for the integration of migrant-specific topics. However, when organizing a survey targeting immigrants, challenges concerning the survey setup are even more accentuated compared to traditional surveys. After a short introduction, this chapter first reviews the literature on survey methodology when targeting migrant populations. Second, it provides an overview of how specific migrant surveys, which have been undertaken in Europe since the year 2000, have addressed different challenges. Third, and considering the first two chapters, it reviews the implementation of the Migration-Mobility Survey by presenting the target population, the sampling procedure, the survey design and the data collection process and response rates. The chapter closes with a short overview of the main socio-demographic characteristics of the participants, thus serving as a basis for the following chapters of this book, and a short preview of the planned second wave of the Migration-Mobility Survey.

Keywords

Survey methodology International migration Under-representation European surveys Switzerland 

2.1 An Increasing Need for Data on International Migrants

The need for information on immigrants and their migration trajectories has risen in recent decades due to an increase in their numbers, in their diversity with respect to their countries of origin and in their reasons for immigration. The need has also risen due to the increasing social, economic and political importance of the foreign population in all European countries, particularly in Switzerland (Font and Méndez 2013, p. 20).

As an alternative to censuses, register data and traditional population surveys, migrant surveys can satisfy this demand for several reasons. They allow for an in-depth analysis of migration flows and migration-related topics such as transnational ties and attachment to the host country, lived discrimination or migratory or settlement intentions – topics that are not covered in censuses or traditional population surveys. Moreover, another shortcoming of traditional population surveys presents low number of migrants in the sample, inter alia because they are a “hard-to-survey” population (Tourangeau et al. 2014).

However, when organizing a survey among migrants, challenges concerning the survey setup are accentuated compared to traditional surveys; the difficulties were emphasized by Font and Méndez (2013), an edited volume reviewing the challenges of migrant surveys performed in different countries and thus contexts. Namely, underrepresentation of certain subpopulations due to under-coverage and non-response poses a serious challenge and thus requires thorough considerations and anticipation.

Despite the methodological challenges, several surveys were launched in recent years in European countries, inter alia the Swiss Migration-Mobility Survey.

Thus, this chapter reviews in Sect. 2.2 the methodological challenges when organizing a survey targeting migrants by discussing the two main components of under-representation, which are under-coverage and non-response. Section 2.3 provides an overview of specific migrant surveys that were undertaken in Europe and how they have addressed some of the methodological challenges, while Sect. 2.4 reviews the survey setup of and the coverage and non-response bias in the Swiss Migration-Mobility Survey. The chapter closes with a short overview of the main socio-demographic characteristics of the participants (Sect. 2.5), serving thus as a basis for the following chapters of this book, and a short preview of the planned second wave of the Migration-Mobility Survey (Sect. 2.6).

2.2 Under-Representation, the Main Challenge When Surveying Migrant Populations

A major difficulty in the production of data on migrants and migration concerns the differing definitions, concepts and measurement parameters used, as emphasized in a study based on data from the migration module of the European Social Survey (ESS) (Davidov et al. 2018). Foremost, the definition of the “target population” varies sensibly from one study to another. Depending on the research goals and financial constraints as well as the legal concepts of migrants in the host countries (Font and Méndez 2013), “migrants” can be defined based on, for example, their nationality, foreign-birth status, recent moves, ethnicity, duration of stay, or reason for immigration. Even when similar or the same definitions are applied, the migration and/or naturalization policies must be considered when interpreting the constructed categories. Thus, the access to citizenship varying from one country to another, defining “migrants” through the nationality may not have the same signification and yield comparable results. This particular challenge is accentuated in multi-site surveys, that is, when several places in sending and receiving countries are involved, to study migration as a dynamic phenomenon (Beauchemin and González-Ferrer 2011).

Additionally, migrant populations share several characteristics of “hard-to-survey” populations (Tourangeau et al. 2014): sampling difficulties, higher mobility, multiple languages, vulnerability and occasionally also reluctance to participate. All of these characteristics can, depending upon the chosen definition, lead to under-representation of certain groups due to under-coverage (at the sampling stage) and/or unit non-response (at the participation stage). For example, whereas some migrants will be difficult to contact due to their high mobility (i.e., highly skilled migrants or expats), others will be difficult to interview due to cultural and/or language differences.

In any case, the survey methods must be adapted to these heterogeneous populations and contexts, not only causing important methodological challenges but also ultimately rendering the production of comparable data very challenging.

2.2.1 Under-Coverage

Coverage biases refer to a non-observational gap between the target population and the sampling frame1 (Groves et al. 2009). Under-coverage occurs when members of the target population are missing in the sampling frame. And according to Groves et al. (2009, p. 70), “perfect frames do not exist; there are always problems that disrupt the desired one-to-one mapping” between the frame and the target population.

Moreover, in some cases missing information hinders performing the desired survey mode or properly specifying the migratory status. Concerning the former, population registers rarely include email addresses, preventing the invitation of participants or the administration of a survey by email. Additionally, in many countries for which a sampling frame is available, the landline telephone coverage is rather low or insufficient. According to Lipps et al. (2013, p. 3), since the 2000s, Swiss survey institutes have faced those challenges due to “mobile-only” households, the increasing replacement of landline service by cell phones and the abolition of the mandatory registration of phone numbers in the public directory in 1998. Concerning missing information on the migratory status, data on ethnicity are very rare in official statistics and the migratory status is mostly defined by the country of origin or the nationality. In some countries, migrants are only registered when intending to reside for at least 1 year in the country (e.g., Sweden).

In the absence of an adequate exhaustive sampling frame, alternative sampling methods can be used such as randomly dialled telephone numbers or random routes.2 Nevertheless, because these methods are not very cost effective, particularly when targeting “rare elements” (such as migrants), other strategies, such as snowball sampling, are proposed to identify the target population. For a comprehensive list of alternative sampling strategies, refer to Reichel and Morales (2017).

However, alternative sampling strategies can introduce a representativeness bias. For example, and many others, snowball sampling must be treated with caution with respect to the representativeness of the target population due to differing probabilities of being selected (Reichel and Morales 2017; McKenzie and Mistiaen 2009). Additionally, sampling strategies using phone directories can introduce a selection bias when people with published phone numbers differ from those whose phone number is not available. In fact, landline phone numbers are more often available than are cell phone numbers. In addition, the level of coverage of landline phone numbers depends on the municipality size (the more urban, the fewer phone numbers listed) and the matrimonial status (singles less often have a number listed than married persons) (Lipps and Kissau 2011). Due to their over-representation among young, single, urban dwellers, migrants are thus more exposed to the problem of under-coverage. Camarota and Capizzano (2004), cited by Lipps et al. (2013), also mention the instability of foreign minorities’ living arrangements and residential situations as a cause of their under-representation (Blohm and Diehl 2001; Morales and Ros 2013). According to Lipps and Kissau (2011), “national minorities usually have a lower likelihood of owning a landline telephone and – even if they do – being listed in the telephone book”.

One possibility of reducing under-coverage consists of proposing either a survey mode that includes all target population elements – for example, a pencil-paper survey when postal addresses are accessible –, a mixed-mode approach, or a multiple frame design, where missing phone numbers in the population registers are added from telephone directories. Based on a sample drawn from the Swiss population register, Lipps et al. (2013) showed that by matching information from commercial telephone directories and external sources (e.g., from the internet), not only was the proportion of missing phone numbers for the frame population reduced from 24% to 14% but the socio-demographic representation of the population was also improved. In particular, they managed to limit the under-representation of the younger age categories, singles, divorced and residents of larger cities.

2.2.2 Unit Non-response

A unit non-response bias occurs in case of a non-observational gap between the sample and the respondents and is particularly problematic when it is correlated with the variables of interest of the study (Deding et al. 2013, p. 173). Non-response can be explained by different reasons: for example, refusal,3 inability to participate due to disability or illness, or non-contact.

Although social and political surveys face increasing non-response rates, the problem is aggravated when surveying migrants due to their lower accessibility (Font and Méndez 2013; Martin et al. 2016). Little is known thus far because few studies have focussed on unit non-response among immigrants, and not all of them have separately analysed non-contact and refusal (Deding et al. 2013, p. 173). In fact, due to the high mobility of several migrant groups, availability depends upon the time spent at home and is reduced by professional, educational and leisure activities (Abraham et al. 2006). For these reasons, young individuals, singles and urban dwellers are more difficult to contact (Stoop 2004; Stoop et al. 2010). Among the explanations of lower response rates among migrants are the increased prevalence of irregular work schedules (Feskens et al. 2006; Deding et al. 2008), longer absences due to visits to the home country (Blohm and Diehl 2001), a younger age structure (Ette et al. 2015) and more-urbanized locations (Morales and Ros 2013). Locating individuals, particularly highly mobile persons, at the address indicated in the sampling frame therefore also depends upon the time elapsed between the drawing of the sample and the fieldwork.

But, inciting individuals to participate depends, particularly for migrants, upon language competences and thus the proposed languages of the survey (Deding et al. 2013), trust in interviewers and the survey institute (Martin et al. 2016), and topic interest (Groves et al. 2009). Concerning certain host country-related topics, migrants might not feel sufficiently concerned to participate, or they might be inhibited from speaking openly about the topics. Moreover, migrants living alone or being socially isolated (Deding et al. 2013; Stoop 2005) are less disposed to participate in a survey. Hence, duration of stay, and eventually naturalization, might increase participation because of better integration (Deding et al. 2013, p. 177).

Another solution to increase willingness can present unconditional (not dependent upon participation) or conditional (only once the questionnaire is filled out entirely) incentives. Nevertheless, the results concerning the effectiveness of incentives are mixed (Bosnjak and Tuten 2003; Singer and Ye 2013; Porter and Whitcomb 2003; Göritz and Wolff 2007). Several studies have shown that the amount, the type, and the timing of the incentives can be decisive for participation in web surveys targeting the general population (Gajraj et al. 1990; Sánchez-Fernández et al. 2010; Scherpenzeel and Toepoel 2012). However, according to Morales and Ros (2013, p. 153), “it is not clear that monetary incentives necessarily increase cooperation rates for immigrants, even if they appear to be successful with native respondents”. A recent feasibility study in Austria showed that incentive strategies are particularly important for panel surveys because of the necessity to keep the respondents as long as possible in the panel (Hosner and Schlechter 2015). A German study on the reaction of ethnic minorities to different incentives has shown that investment in a double incentive strategy (conditional and unconditional) reinforces reciprocity and trust in the usefulness of participation (Fick and Diehl 2013). Furthermore, a survey from the United Kingdom showed that respondents who received an incentive (in the form of a stamp book) were more likely to cooperate than were those who did not. Finally, concerning the influence of incentives on participation, they found no significant differences between sub-groups (regional or ethnic minorities) (Department For Communities And Local Government 2012).

The participation can also vary or depend on the survey mode that is proposed. According to Czaja et al. (2014), telephone surveys remain among the most widely used methods of data collection. Among other reasons, they permit reaching people who lack access to the internet or to a computer or who have difficulties with writing and/or reading. In addition, the interviewer can convince some reluctant individuals to take part in the survey. The same is also true for face-to-face interviews. In contrast, self-administered surveys, for example, online surveys, increase the participation of difficult-to reach or unreachable individuals and offer a greater flexibility concerning place and time of participation.

The main challenge of online surveys refers to the digital divide, in which access to and the use of the internet vary substantially between groups (Dillman et al. 2014). Highly educated, computer-literate individuals with more up-to-date equipment (Czaja et al. 2014), younger age categories (Ette et al. 2015) and men compared with women are more likely to complete an online questionnaire. According to Martin et al. (2016), little is known concerning access to and the use of the new technologies by people with an immigrant background. However, one could argue that migrants present a higher affinity with modern communication tools and the internet because these technologies facilitated not only planning the move from one country to another but also maintaining contact with family and friends in the origin country (Ette et al. 2015; Mau and Mewes 2007).

A solution to limit unit non-response is to propose a mixed-mode design. Because financial constraints are among the most important limitations concerning mode choice, one possibility is to begin with a cheaper mode of data collection and then to use a more expensive mode for the non-respondents from the first mode (sequential mixed-mode). Another possibility consists in proposing two modes simultaneously and from the beginning (concurrent mixed-mode). When the combination of computer-assisted telephone interviews and a face-to-face or mail survey exceed the budget, an online survey appears to be the most cost-effective alternative, not least because of important cost-saving potential for the data-entry procedure (Dillman et al. 2014).

Nevertheless, when proposing a combination of self- and researcher-administered questionnaires, measurement bias can occur when, due to differing degrees of interviewer involvement, one group is better (resp. less) informed than is the other or participants are inhibited from answering more-sensitive questions when talking to a researcher (Groves et al. 2009, p. 141). Moreover, every mode has its own tradition of question formats, which can affect response distribution. Thus, measurement equivalence needs to be ensured by questionnaire equivalence, that is, question design, layout and instructions used in the main mode needs to be adapted to the auxiliary mode (see Dillman et al. 2014, pp. 232–240).

2.3 European Migration Survey Landscape

Thus far, long-term integration of immigrants has primarily been addressed by qualitative non-representative surveys at the local level (Jacobs 2010). However, in recent years, quantitative surveys have been launched in European countries (Mendez and Font 2013) to cover the scientific and political need for adequate data among migrants or foreigners. What follows is an overview of the such surveys. This inventory is the result of a rigorous search. On the one hand, we consulted PROMINSTAT,4 a compilation of meta-information on statistical datasets on migration, integration and discrimination in 29 European countries, which was financed by the European Union. In addition, we consulted the Swiss Centre of Expertise in the Social Sciences (FORS) Database,5 which refers to international surveys that are conducted for Switzerland. On the other hand, we performed an online search including a keyword6 search on Google and Google scholar and an analysis of comprehensive overviews of survey methods (Mendez and Font 2013; Bonifazi et al. 2008; Reichel and Morales 2017).

We only considered quantitative surveys that were performed after the year 2000.7 In addition, we excluded surveys targeting children of immigrants and undocumented migrants, refugees and asylum seekers because the survey methodology that must be deployed to reach them varies significantly from other migrant categories.

The final list of surveys described in this chapter (Table 2.6 in the Appendix) includes a majority of surveys among migrants in Europe. Nevertheless, it is not exhaustive, particularly because small surveys at the regional level are missing.

Depending upon the general research goal, the specific demographic and thematic foci and financial and time constraints, varying survey methods were deployed. First, we can distinguish between migration modules added to existing surveys and specific migration surveys. Modules allow for an extension of the original questionnaire and therefore a better coverage of migration-related topics, a strategy that also has a positive effect on the response rate because migrants may feel more concerned with the questionnaire and thus the survey in general. Examples include the migration and minority module of the European Social Survey (ESS), the module on the labour market situation of migrants and their immediate descendants of the European Labour Force Survey (EU-LFS), and the Migration Module of the German Socio-Economic Panel (IAB-SOEP). Among the immigrant surveys that were identified, we find several multi-national surveys, such as the European Union Minorities and Discrimination Survey, Immigrants and Ethnic Minorities in European Cities: Life-courses and Quality of Life in a World of Limitations (LIMITS), the Six-Country Immigrant Integration Comparative Survey (SCICS), and other specific national surveys.

One of the major challenges when surveying a specific population is to define a sampling strategy that guarantees the representativeness of the target population. In a few countries, such as Switzerland or Sweden, population registers allow for national coverage and if necessary also the oversampling of specific groups (e.g., Longitudinal Internet Studies for the Social Sciences (LISS) Panel and LISS Immigrant Panel). The oversampling strategy is also an often-applied strategy in already-existing surveys, even more so when a migration-related module is added (e.g., IAB-SOEP or SLFS) (Kraler and Reichel 2010).

In cases in which exhaustive sampling frames are missing (or are out-dated), other strategies must be developed. Several countries base their sampling on census data. Nevertheless, due to an absence of addresses and telephone numbers in the census data, this strategy implies, in a second stage, a time- and cost-intensive household screening (e.g., Spanish National Immigrant Survey) or randomly dialled telephone numbers (e.g., France in the Immigrant Citizen Survey). Other projects randomly selected migrant groups in centres of aggregation8 (for instance, in Milan in the LOCALMULTIDEM project) or selected individuals based on their names from the telephone directory (Six Country Immigrant Integration Comparative Survey or Lyon in the LOCALMULTIDEM project) (Lynn et al. 2018). The NIDI/Eurostat Push and Pull Factors of International Migration project combined different sampling strategies, first, by using a disproportionate sampling in order to oversample sample areas with a high prevalence of migrant households and second, by adding a snowball sampling procedure in cases in which few migrants were found in the screened areas (Groenewold and Bilsborrow 2008). The latter allowed boosting the response rate of migrants and thus to complete strata characterized by a high-nonresponse (Reichel and Morales 2017). Also, targeting a specific population, as was for example done in Europe in the Pew Global Attitudes Project regarding Muslims in Europe (Pew Research Center 2006) where they oversampled Muslims, can reduce the coverage problem. This approach allows focussing on a highly relevant topic for the surveyed population and therefore reach a higher response rate. However and more generally, when the target population is too narrowly defined, an absence of specific characteristics in the sampling frames might prevent accurate sampling (e.g., religion, occupation and/or education).

Even though international migration is a transnational phenomenon, most surveys are performed in the destination country, while some investigate the origin-country perspective. A very few actually collect information on both sites to obtain a global picture of migration (Beauchemin et al. 2013). The latter is particularly subject to sampling problems because it requires sampling in both geographical places. In addition, the sampling frame must be representative of the host and sending country, and thus reaching a sufficient number of individuals in the final sample is even more challenging.

The MAFE project (Migration between Africa and Europe) is an example of a multi-site survey, covering three countries of origin in Africa (Senegal, DR Congo and Ghana) and six destination countries in Europe (France, Spain, Italy, Belgium, Great Britain and the Netherlands). Due to missing sampling frames in most of the countries (except for Spain) and to obtain a representative sample of the target population, several strategies were applied. Although in the countries of origin individuals were selected using Census Data or the 2007 DHS Survey Data, in the destination countries, quota sampling methods (age, gender and target areas) and complementary methods such as origin-based snowballing or time-location sampling methods9 were applied (Reichel and Morales 2017; Schoumaker et al. 2013).

Transnational snowball sampling in the context of African-European migration has an important representativeness bias because migrants with closer links with their migrant community are over-represented (Beauchemin and González-Ferrer 2011). Thus, results might be biased such that the effect of migration on transnational financial transfers, poverty in households at the origin, and the influence of previous migration experiences on the individuals’ propensity for out-migration are all overestimated.

Another cross-sectional multi-country survey is the already mentioned NIDI/Eurostat Push and Pull Factors of International Migration. This survey was conducted in Italy and Spain (both countries of destination), Morocco, Egypt, Turkey, Ghana and Senegal (countries of origin). The survey was also subject to the same three constraints: geographical dispersion of migrants, migrants being rare elements, and the lack of sampling frames. Thus, the researchers identified and oversampled regions with a high density of migrants (Schoorl et al. 2000; Groenewold and Bilsborrow 2008).

Despite an increasing effort to launch longitudinal surveys, most of the immigrant surveys are cross-sectional. However, to measure social and structural integration, longitudinal surveys appear more suitable (Jacobs 2010). Moreover, cross-sectional surveys often have a validity problem due to method variance biases (variances that are due to the measurement method) and the impossibility of causal insights. Both methodological biases could be reduced by using longitudinal data because of the separation by time of the outcome and predictor, which guarantees a causal inference and minimizes the bias due to respondent moods or response styles and the survey context (Rindfleisch et al. 2008; Podsakoff et al. 2003).

Although, due to regular follow-ups, longitudinal surveys require higher financial and time investment compared with cross-sectional surveys, a handful of migration-related longitudinal surveys have been launched in Europe, such as the Longitudinal Survey of the Integration of First-Time Arrivals (ELIPA, France) and the Dutch LISS Panel.

The Dutch LISS Panel started in 2008. To ensure adequate coverage, the survey used a register-based sample and selected individuals were initially contacted in person or by phone for a 10-min interview. Then, participants could switch modes and continue online. To include participants without internet connection, access equipment was loaned (Scherpenzeel 2011). In 2008, no particular attention was given to the foreign population (no oversampling strategy and Dutch as survey language), which led to their lower participation, in particular among non-Western migrants. The LISS Immigrant Panel, which followed-up in 2010, stratified the population by country of origin, including first and second generations of four non-European immigrant groups (Moroccan, Turkish, Surinamese and Antillean) (Das 2012; De Vos 2010).

The French ELIPA study started in 2010 and was repeated in 2011 and 2013. This large-scale survey aimed at gathering information on recently arrived migrants from a longitudinal perspective. The survey was translated into numerous languages – Arabic, Turkish, Chinese, Russian, English, Serbian, Tamil, Bengali, Spanish, Vietnamese, Thai, Albanian, Soninke and French – a strategy that allowed covering 95% of the migrant population in France. Like the LISS Panel, the longitudinal approach of the survey required long-term adherence of the participants, which was supported by telephone and personal pre-recruitment and a retention strategy through information letters between waves (Panel Care).

Another challenge of longitudinal surveys refers to the risk of attrition (dropout). As discussed, dropouts due to non-response in case of high mobility behaviour can lead to biased right-censuring (Rindfleisch et al. 2008). An intensive Panel Care and a tracking strategy to re-contact individuals who have moved is needed to guarantee a satisfying response rate for the follow-ups (Lee 2003). The ELIPA survey reached for instance a response rate of 78% for the second wave. However, other non-migrant-specific surveys have shown that over a long period, the attrition increases significantly; in the case of the German Socio-Economic Panel, for example, the response rate reached in the first wave in 1984 was 70% but dropped to 25–45% in 2004.

Time-and-money-consuming longitudinal surveys are occasionally also replaced by retrospective questionnaires in cross-sectional surveys – for example, in the surveys Parcours et profil des migrants (PPM), Trajectoires et Origines (TeO), and NIDI/Eurostat Push and Pull Factors of International Migration of, or the Immigrants and Ethnic Minorities in European Cities: Life-courses and Quality of Life in a World of Limitations (LIMITS). The last is a longitudinal and comparative survey launched in 2005/2006 in six European cities: Amsterdam, Bielefeld, Lisbon, Rotterdam, Stockholm and Vienna. It aims to describe migrants’ life trajectories and includes migrants from the cities’ four main sending countries (Turkey, Morocco, Serbia, and Cape Verde) who lived for at least 15 years in the receiving country. To ensure comparability of the data, experts of all countries proposed a cross-national sampling design but with country-specific sampling strategies.10 LIMITS includes a retrospective questionnaire based on a life-history calendar that identifies specific events in a visual and user-friendly form. Nevertheless, this approach can be problematic due to the whitewashing, or downgrading, of subjective impressions (i.e., the satisfaction of the move to the host country) and to the participant’s incapacity to remember past events (Blossfeld and Rohwer 2002; Glasner and van der Vaart 2009; Latcheva et al. 2006).

A further challenge concerns divergent sensibilities with respect to the adequate survey method due to the diversity of the migrant population in terms of nationalities, age, education and socioeconomic position. The choice of the survey mode, incentives and proposed language(s) might have a decisive effect on the participation of migrants (Hosner and Schlechter 2015) and thus on the representativeness of the survey.

Although the design varies considerably from one survey to another, most of the surveys chose computer assisted face-to-face interviews (e.g., Immigrant Citizen Survey, German Socioeconomic Panel, Longitudinal Survey of the Integration of First-Time Arrivals in France, and Spanish National Immigrant Survey) or a mixed-mode approach (e.g., LISS Panel (online using particular designs e.g., Smartphone, GPS and telephone) and Diskriminierungserfahrung in Deutschland (face-to-face and online)). The Italian Survey on Social Conditions and Integration of Foreign Citizens of 2012 (SCIF) added a self-administered web survey to the paper-pencil mode to reach young participants. A strategy that not only allowed interviewing several persons simultaneously but also reducing the duration of the interview (Conti et al. 2017).

Few surveys use incentive strategies (e.g., the Generation and Gender Survey (GGP) or the LISS Panel). For instance, the LISS Panel used a prepaid incentive strategy to increase participation rates, adding to the first invitation letter ten Euros and a promise of an additional ten Euros when completing the questionnaire. According to the researchers, a higher amount of money did not significantly increase the response rate (Scherpenzeel 2011). In the end, the response rate for the first wave was 75%, and for the second wave 48% (Scherpenzeel 2011).

Finally, the language(s) proposed in a survey might influence participation willingness. As done in most migration surveys and modules, the Migration Module of the German Socio-Economic Panel in 2013 and 2015 (IAB-SOEP) proposed for example a questionnaire in the predominant languages of the target population (English, Polish, Turkish, Rumanian and Russian).

A last major and increasing challenge in a globalizing world relates to the production of comparable data at the European level. As already mentioned, definitions of migrants can vary across Europe, which hinders or at least impedes such conjoint data collections.

A major effort gathering data to evaluate the European integration policy was made by the cross-sectional and transnational Immigrant Citizen Survey (Huddleston and Dag Tjaden 2012). The survey occurred in 15 cities in seven EU states (Belgium, France, Germany, Hungary, Italy, Portugal and Spain) and included migrants who have lived for at least 1 year in the destination country and who held a residence permit. In addition, the sampling strategy, which was inspired by the European Social Survey (ESS), was based on either the country of birth or the nationality, using the best national sources available (register data or censuses).

Another comparative survey, the European Union minorities and discrimination survey (EU-MIDIS), collected data on discrimination faced by ethnic minorities in the EU. Due to the varying proportion of the immigrant population in the different European countries, the geographical coverage varies from one country to another (cities, nationwide or metropolitan areas). To be included in the survey, two main criteria had to be fulfilled: high vulnerability and membership in the largest immigrant groups. The sampling strategy, to survey 500 individuals per group, was adapted to the particularities of each member state, varying from register data, random route sampling, and focussed enumeration11 to network sampling (European Union Minorities and Discrimination Survey (EU-MIDIS) 2009).

As shown by this short review, all particularities and challenges when surveying migrants were handled in different ways. Standard survey procedures must be adapted to the population under study and the specific conditions of the country and project-related budget and time constraints. Section 2.4 of this chapter explains how these issues were addressed when setting up the Migration-Mobility Survey.

2.4 The Migration-Mobility Survey

Due to the economic and social importance of recently arrived and highly skilled migrants in Switzerland and to the lack of systematic research on their behalf, the nccr – on the move performed the Migration-Mobility Survey in 2016.12

The survey covers the main origin groups of recently arrived migrants (up to 10 years). It gathers information on the migratory history, citizenship intentions, education and employment history and current situation, family configuration and household situation, integration (language skills, personal network and transnational ties, leisure activities, and civic engagement), and life in Switzerland (see Table 2.7 in the Appendix for an overview of the questionnaire). The questions were adapted from international and/or Swiss surveys, allowing the researchers to compare their results with other survey data. The survey focussed on the most numerous immigrant groups, including German-, French-, Italian-, English-, Spanish- and Portuguese-speaking nationals. To reach those groups adequately and to guarantee the representativeness of each group, a mixed-mode approach of an online survey and telephone interviews proposed to occur in all six languages was selected. The survey was conducted by the LINK Survey Institute between October 7, 2016, and January 9, 2017. Ultimately, the goal set was largely outreached, with 5973 completed interviews, of which 97% were conducted online.

What follows is a short description of the survey setup (target population, sampling, and design), the data collection and response rate analysis, and a brief presentation of the participant’s socio-demographic characteristics.

2.4.1 Target Population

It was decided to propose a questionnaire in six languages to cover the most numerically important foreign-born groups of Switzerland. Table 2.1 provides an overview of the nationalities, the attributed languages and their share in the total foreign-born population. First, considering German, French and Italian – which are also the three main languages spoken in Switzerland – allowed us to include the most important immigrant groups from Switzerland’s neighbouring countries (Germany, Austria, France and Italy). Second, by including English, we were able to reach North American (US/Canada) and Indian citizens. Third, Spanish and Portuguese, the mother tongues of further numerically important immigrant groups originating from Spain and Portugal, were included. Based on these six languages, it was further decided to include for comparison two groups from non-industrialized and non-EU/EFTA countries: West Africa (French-, Portuguese- or English-speaking countries) and South America (Spanish-, English- or Portuguese-speaking countries).
Table 2.1

Geopolitical region, origin groups, attributed language and share in the total foreign-born population

Geopolitical region

Regions and countries

Attributed language

Share in total populationa

EU(EFTA)

1. Germany

GE

23%

2. Austria

2%

3. France

FR

8%

4. Italy

IT

9%

5. United Kingdom

EN

3%

6. Spain

SP

3%

7. Portugal

PO

13%

Industrialized non-EU/EFTA

8. North America

Canada, USA

EN

2%

Non-industrialized non-EU/EFTA

9. India

EN

1%

10. West Africa

Benin, Ivory Coast, Guinea, Mali, Niger, Burkina Faso, Senegal, Togo

FR

1%

Gambia, Ghana, Liberia, Nigeria, Sierra Leone, Saint Helena

EN

 

Guinea-Bissau, Cape Verde, Sao Tome and Principe

PO

 

11. South America

Argentina, Bolivia, Chile, Ecuador, Colombia, Paraguay, Peru, Uruguay, Venezuela

SP

3%

Guyana

EN

 

Brazil

PO

 

Note: aThe share in the total foreign-born population considers other selection criteria (age at immigration and the time of the survey, specific resident permit and max. ten years of residence)

Source: Migration-Mobility Survey 2016

The survey focussed on immigrants who had arrived in the last 10 years (after June 2006) and held a resident permit (B), settlement permit (C), short-term permit (L), were diplomats/international civil servants or the latters’ family members (Ci) at the time of immigration. Due to the survey’s aim and methodology, only “voluntary” migrants were considered and thus asylum seekers (N), provisionally admitted persons (F), and persons without a legal residence permit were excluded. Not only was the questionnaire not adapted to their specific migratory trajectory and life situation, but the methodology (for example, with respect to languages and sampling frame) would also have required alteration to reach and survey these specific populations.

Finally, only immigrants aged 18 years or more at the time of immigration – presuming that minors do not independently decide on their migration – and aged between 24 and 64 years at the time of the survey were included. Based on these criteria, the 11 selected groups represent 68% of the total foreign-born population.

2.4.2 Sampling

The Swiss Federal Statistical Office’s (SFSO) sample register (SRPH),13 which is drawn from the harmonized registers of persons of the Swiss Confederation, the cantons and the municipalities, served as the sampling frame. The frame is an exhaustive list of persons living in Switzerland, providing the above-mentioned inclusion criteria for the target population. Finally, register-based sampling allows the calculation of post-stratification weights.

To reach representative samples for the 11 origin-groups, a stratified random sampling strategy was applied. Concerning simple random sampling, each element in the sample has a known and nonzero probability of being chosen. However, key subgroups are represented, guaranteeing greater precision and improving therefore the potential for the units to be more evenly spread over the population. Concerning recently arrived immigrants (less than 2 years of duration of stay) and to anticipate their lower participation rates and higher attrition rates when conducting a second wave (see Sect. 2.7), we decided to oversample individuals who had recently arrived in Switzerland. Therefore, the representation of the length of stay (up to 2 years and 2–10 years) and both genders were ensured by a disproportionate stratification. The West African group presents an exception because the size of this population does not allow a stratification based on the duration of stay. In total, we defined 42 strata.

The targeted sample size for every group was set between 352 (West Africa) and 384 (Germany) interviews, setting 4130 desired interviews. Based on a recent survey with German migrants in Switzerland (Steiner, Ilka. 2017. Immigration and settlement? German Migration Flows to and from Switzerland under the Provision of Free Movement of Persons. Unpublished PhD-Thesis, University of Geneva), we drew 13,660 addresses, that is n × 3.5 for all groups, except for the Portuguese (n × 6) because studies in Switzerland have shown lower response rates for this group (Fibbi et al. 2010).

Moreover, reserve samples for all 11 sub-groups (n = 6476) with a similar structure as the main sample were drawn. These additional addresses aimed to guarantee the achievement of the survey’s minimum sample size and at ensuring a sufficient number of interviews for each stratum in the event of too-low response rates. The samples were drawn by the SFSO in early September 2016 based on the list of address available in the register at the end of June 2016.14

2.4.3 Survey Design

Due to the low coverage of phone numbers for our target population in the sampling frame (16%), we opted for a mixed-mode approach in which the collection of information was primarily centred on an online questionnaire (CAWI) that people could complete using different electronic devices. This approach was complemented with telephone interviews (CATI) on request from the participants.

As a first step, all selected persons received an invitation letter asking them to participate in the online survey. The letter provided a link and a personalized access code, allowing persons to access the online questionnaire directly. Although favouring the online survey, the letter also mentioned the possibility to participate by calling a toll-free hotline, either to convene an appointment for an interview or to participate directly in a telephone interview. In addition, throughout the fieldwork, people contacting the hotline could still participate by CAWI. In this case, the person was asked to indicate his/her email-address and then received an email containing the online access link immediately after having called the hotline. Finally, when people only partially completed the online survey, their username and personal code allowed them to return to the questionnaire and to resume participation from the last question before quitting. However, once the questionnaire was completed, the personal access was closed.

To stimulate participation, two additional letters were sent to non-respondents (see Table 2.2): a first reminder approximately two and a half weeks after the invitation letter and a second reminder approximately two and a half weeks after the first reminder.
Table 2.2

Number and date of letters sent

 

Main sample

Reserve sample

Total

Total (N)

Date

Total (N)

Date

Invitation letters

3500

06.10.

4110

21.11.

17,722

10,112

13.10.

1st reminder

11,869

01.11.

3586

6.12.

15,455

2nd reminder

8986

24.11.

2791

19.12.

11,177

 

44,954

Source: Migration-Mobility Survey 2016

Each selected person received the invitation letter in two languages: the main language of their country of nationality and, if different from the latter, the main language spoken in the municipality of residence.

Concerning the incentives strategy, three tablets were randomly attributed to participants agreeing to participate in the draw. Moreover, a flyer describing the research purpose accompanied the first invitation letter. Moreover, additional information was made available on the survey’s webpage (http://nccr-onthemove.ch/research/migration-mobility-survey/). Finally, a newsletter to keep participants, researchers and other interested people informed was set up. The information material (invitation letters, flyers, and webpage) was also available in all six survey languages.

2.5 Participation and Non-response Rate

At the end of the fieldwork, 5973 interviews were completed, surpassing the goal of 4120 interviews (see Table 2.3). In total, 4702 people of the main sample participated. Moreover, the use of the reserve sample – of which 1271 people participated – helped not only to increase the number of interviews but also to achieve the target set for each of the strata, with the sole exception of German men with a duration of residence of less than 2 years. In general, the participation rates of recently immigrated men were among the most deficient strata.
Table 2.3

Status of sampled addresses, for the main sample, reserve sample and the total (N)

In total, more than one-third of the sampled individuals with valid addresses participated, with total response rates ranging from 26% for the Portuguese sub-sample to 45% for the Indian sub-sample (see Fig. 2.1).
Fig. 2.1

Response rates by group of origin (in %)

Note: response rate calculated according to AAPOR (American Association for Public Opinion Research, AAPOR response rates, https://www.aapor.org/Education-Resources/For-Researchers/Poll-Survey-FAQ/Response-Rates-An-Overview.aspx. Accessed 15 June 2018)

Source: Migration-Mobility Survey 2016

This participation rate can be considered satisfactory given the characteristics of the population under study (mobility and low rate of landline numbers for the reminders). Moreover, the age and gender structure of the sample and the respondents being very similar, the results guarantee a certain degree of representativeness within each group.

The mean duration of the online interviews was 35 min and 20 s. The correlation between the assigned language (based on the nationality) and the interview language was high, thus validating our strategy; only 1.1% of individuals who were assigned German chose another language, 1.2% for French, 4.6% for English, 5.1% for Italian, 14.8% for Spanish and 16.3% for Portuguese.

To identify a possible non-response bias, logistic regression analyses were applied to identify the most influential variables for non-response (see Table 2.4) Variables tested included nationality, age, gender, residence permit and civil status. All variables had a significant effect on the response rate, although to varying degrees. In fact, women showed a significantly higher response rate than did men. Short-term permit holders and international diplomats presented a higher risk of non-response than did other permit holders. This can be explained by their higher mobility and shorter duration of stay. Additionally, specific nationalities, such as Spain, Portugal and South America, had a significantly higher risk of non-response. Compared with the other nationalities, these groups were also more frequently employed in low-skilled jobs and held a tertiary degree less often. These results are confirmed by several studies that conclude that an underrepresentation of national minorities results in bias, even after controlling for socioeconomic status (Elcheroth et al. 2011; Lipps and Pollien 2011; Laganà et al. 2013).
Table 2.4

Logistic regression results for unit non-response (odds ratios)

 

Odds ratio

Sig.

Nationality (ref. Germany)

 Austria

1.11

 

 France

0.98

 

 Italy

0.86

**

 United Kingdom

0.86

+

 Spain

1.18

*

 Portugal

1.89

***

 North America

0.88

 

 India

0.70

**

 West Africa

1.28

 

 South America

1.28

**

Gender (ref. men)

 women

0.78

***

Age (ref. 24–34 year)

 35–44 years

0.88

**

 44–54 years

0.95

 

 55–65 years old

1.10

 

Residence Permit (ref. residence permit B)

 Settlement permit C

0.96

 

 Short-term permit (L)

1.41

***

 DFAE and Ci permit

1.90

**

Civil status (ref. married)

 single

1.06

 

 divorced/separated/widowed

1.30

**

Note: *** p < 0.001, ** < 0.01, *p < 0.05, + p < 0.1

Source: Migration-Mobility Survey, 2016. Weighted results (design weights)

We also observe a weak (35–44 years compared to 24–34 years) or no difference (44–54 years and 55–65 years compared to 24–34 years) between the age categories and a slightly lower likelihood of participation among divorced or widowed participants compared with married individuals.

Duration of residence was not included in the regression model because it was highly correlated with the resident permit. Nevertheless, a descriptive analysis of the response rate by duration of residence shows a U-shape relationship. The proportion of respondents is 32% among individuals who stayed up to 1 year in Switzerland, 43% among migrants with five to 6 years of residence, and 36% for the participants that stayed between 9 and 10 years.

Finally, based on the analyses presented in this section, a weight variable was calculated following a three-stage process: considering the probability to be selected in the sample (design weight), correcting for non-response (non-response rates) and adjusting for the size of the reference population (calibration). For more-detailed information on the weighting procedure or other aspects of the methodology, please consult the survey report, which is available online.15

2.6 Socio-demographic Characteristics of Participants

Table 2.5 presents the socio-demographic characteristics of participants by origin. First, although slightly more men participated than did women, the distribution varies considerably from one origin group to the other. For Germany and France, the men’s share is as high as or even surpasses 60%. North Americans and West Africans present rather balanced gender ratios. The only origin group in which the men’s share is lower than the women’s share is migrants from South America, with 28%. This share nevertheless represents the ratio observed in these populations, indicating a highly feminized migration flow. In fact, the largest differences between participation and population structure are found for German men, who participated more often (61% vs. 56%) and West African men, who participated less often (51% vs. 56%).
Table 2.5

Socio-demographic characteristics of participants, by origin (in % if not otherwise indicated)

 

Germany

Austria

France

Italy

United Kingdom

Spain

Portugal

North America

India

West Africa

South America

Total

%

N

Gender

 Men

61

53

60

63

56

54

56

48

58

51

28

54

3199

 Women

39

47

40

37

44

46

44

52

42

49

72

46

2774

Mean age* (in years)

40.5

39.0

38.0

39.0

39.0

39.0

38.0

42.0

35.5

36.5

38.5

mean = 39.0

Civil status

 Single

15

13

13

14

10

15

7

7

7

7

4

10

609

 Single with Partner

28

32

33

26

17

21

17

9

3

4

6

18

1085

 Married

44

41

46

49

67

56

67

78

89

75

81

63

3738

 Divorced or widowed with or without partner

13

14

9

11

7

8

9

7

2

13

9

9

541

Education

 No, compulsory or secondary II

28

33

15

44

9

38

76

6

3

56

49

32

1914

 Tertiary

72

67

85

56

91

62

24

94

97

44

52

68

4059

Residence permit

 Settlement permit (C permit)

53

41

34

27

38

20

27

26

11

16

24

29

1739

 Residence permit (B permit)

45

56

62

69

58

71

63

65

67

77

70

64

3789

 Diplomat or International Status (DFAE permit) or residence permit with gainful employment (Ci permit)

0

1

0

1

2

2

0

4

2

7

2

2

106

 Short-term residence permit (L permit)

2

2

3

4

2

6

9

5

20

0

3

5

318

Duration of stay

 Less than 2 years

13

18

23

26

14

18

11

26

31

16

17

19

4077

 Two years or more

87

82

78

74

86

82

89

74

69

84

83

81

1896

Total (N)

546

579

560

572

525

530

583

570

573

410

525

100

5973

Note: *Rounded values to 0.5

Source: Migration-Mobility Survey, 2016. Unweighted results

Considering that only migrants aged between 24 and 64 years at the time of the survey were included, the mean age of all participants is 39 and ranges from 35.5 years for Indians to 42 years for US/Canadian-citizens. No differences in the age structure of British respondents and the British population are observable, with a mean age of respectively 43 years.

Concerning civil status, 28% of all participants are single, of whom 18% are in a relationship, whereas more than half are married (63%). The highest shares of married participants are observed among extra-European country nationals (between 75% for West Africans and 89% for Indians) and the lowest among citizens from Switzerland’s neighbouring countries (between 41% in Australia and 49% in Italy). Nevertheless, the latter often live in a partnership without being married; in fact, they present above-average shares for singles who are in a partnership (between 26% for Italy and 33% for France). Once weighted (considering that civil status was considered when computing the weight variable), the share for the married participants is ten percentage points lower in the total population (53%), but no large differences are observed when separately considering the origins.

In total, 68% of participants hold a tertiary degree. High above-average shares are found for the English-speaking origin groups, that is, India (97%), North America (94%) and Great Britain (91%), followed by France (85%) and Germany 72%). The lowest shares are observed for the Portuguese citizens (24%) and West Africans (44%). Except for the total share – that is, 62% after weighting – we find the same educational distribution by origin once the data are weighted.

Although a bit less than one-third of all participants holds a settlement permit (C), two-thirds hold a residence permit (B). Germans present the highest share of settlement permit holders (53%), which is also reflected by their higher mean duration of residence – in fact, EU/EFTA citizens obtain a settlement permit “almost” automatically after a residence duration of 5 years. However, once weighted, this share is much lower at 46%, whereas the one for the residence permit holders increases from 45% for the participants to 51% for the weighted results. Although the Spanish citizens present the highest shares of residence permit holders compared with all EU/EFTA nationals (71%, a share that does not change after weighting), 77% of all West African participants hold a residence permit (slightly lower after weighting at 73%). A last result concerns the short-term permit holders (L). In fact, one of five Indians and one of ten Portuguese hold such a permit, indicating their involvement in more short-term employment engagements. Nevertheless and when considering the educational distribution, the Indians are instead working in highly qualified jobs, whereas the Portuguese are in less-qualified jobs.

Finally, concerning the duration of stay, Indians (31%), North Americans and Italians (each 26%) present the highest shares of participants that have thus far stayed for less than 2 years in Switzerland, whereas the Germans’ (13%) and Portuguese’s (11%) shares are among the lowest. Although once weighted, the shares are slightly higher (due to the higher non-response of individuals with a short(er) duration of stay, the ranking does not change – e.g., Great Britain 34% and Portugal 12%).

2.7 Conclusion

The Migration-Mobility Survey joins other surveys initiated in other European countries, providing innovative data on recently arrived migrants to Switzerland with respect to their migration trajectory and integration process in Switzerland. As done in other surveys, standard survey procedures were adapted to the migrant population, the specific conditions of Switzerland and the research objectives of the nccr – on the move.

In the future, the Migration-Mobility Survey will be extended. A second wave will occur in autumn 2018. Its aim is threefold. Participants of wave 1 who agreed to be interviewed again (62.5% of all participants) and remained living in Switzerland will be asked questions on their current labour market situation and social integration, to measure the pace of integration. Also, participants not living in Switzerland anymore will be asked about their emigration motives. Finally, a new sample of migrants that is representative of the whole immigrant population will be drawn, whereas the 11 origin groups are kept for comparative reasons with wave 1. A questionnaire similar to that used in wave 1 will be applied. Indeed, attrition is expected to be relatively high in the first wave because of re-emigration. To obtain – in the future – longitudinal results consistent with our research objectives, we must ensure the ability to include in 2020 and thereafter a sufficient number of immigrants in the longitudinal Migration-Mobility Survey.

Footnotes

  1. 1.

    A sampling frame is the source material from which a sample is drawn. It is a list of all those within a population who can be sampled (elements) and can include individuals, households or institutions. A typical sampling frame is the population register.

  2. 2.

    Interviewers are assigned with a starting location and provided with instructions regarding the direction or the side of the street as well as the selection procedure of the households.

  3. 3.

    For a list of factors influencing willingness, see p. 170 of Groves et al. (2009).

  4. 4.

    Prominstat database, http://www.prominstat.eu/drupal/node/64. Accessed 31 October 2017.

  5. 5.

    FORS-Database, http://forscenter.ch/de/our-surveys/. Accessed 15 December 2017.

  6. 6.

    Key words used and combined in the online search for surveys: Survey, immigrants, ethnic minorities, integration, labour market, highly skilled migrants, living conditions, migration, foreigners, migrant sample, migrant modules, hard-to-reach, hard to survey, longitudinal or cross-sectional survey.

  7. 7.

    The only exception is the NIDI/Eurostat Push and Pull Factors of International Migration, which occurred in 1996/1997.

  8. 8.

    Identifying a list of places (centre of aggregations) where the target population concentrates. The final selection of the respondents at the identified place is random.

  9. 9.

    Method used to sample hard-to-reach populations by choosing locations where the target population concentrates (at different times) (e.g., Centres of aggregations).

  10. 10.

    Random sampling through registers (Amsterdam, Rotterdam and Bielefeld) or snowball sampling, imposing different entrance points.

  11. 11.

    To render the random route procedure more efficient, identified persons are asked to “map” the neighbours that correspond to the target population and therefore to identify easily areas where migrants live.

  12. 12.

    Further information can be obtained on the webpage of the nccr – on the move (http://nccr-onthemove.ch/research/migration-mobility-survey/) or on FORSbase, a Swiss data-access portal (https://forsbase.unil.ch/project/study-public-overview/14592/0/). Both accessed 14 June 2018.

  13. 13.

    Statistikerhebungsverordnung https://www.admin.ch/opc/de/classified-compilation/19930224/index.html. Accessed 15 March 2017.

  14. 14.

    Moreover, an additional sample of 600 addresses was drawn in July 2016 for a pilot. The pilot’s aim was to test the general reception of the survey, the programming and filters, and the length of the questionnaire, rather than question wording and comprehension, although the last two were also considered.

  15. 15.

    FORSbase, Migration-Mobility Survey, https://forsbase.unil.ch/project/study-public-overview/14592/0/. Accessed 14 June 2018.

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Authors and Affiliations

  1. 1.Institute of Demography and Socioeconomics IDESOUniversity of GenevaGenevaSwitzerland
  2. 2.nccr – on the moveNeuchâtelSwitzerland

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