All time references are in CEST
Sampling migrants in general population surveys 2 |
|
| Session Organisers | Professor Francesco Molteni (University of Milan, Department of Social and Political Sciences) Professor Riccardo Ladini (University of Milan, Department of Social and Political Sciences) Professor Ferruccio Biolcati (University of Milan, Department of Social and Political Sciences) |
| Time | Thursday 20 July, 16:00 - 17:30 |
| Room | U6-10 |
Sampling migrant groups is one of the biggest challenges in survey research. Such a challenge has been tackled by relying on both probabilistic and non-probabilistic research designs focusing on specific sub-populations (often with a local focus). With very few and remarkable exceptions (i.e., the BHPS and the SOEP), what is lacking when studying migrant populations with survey methods is the comparison with the native population, which is a key issue when looking at the differences as well as the similarities between the groups. Because of this, one of the main approaches to perform such a comparison has been to refer to the subsamples of people of foreign origin from general population surveys samples. Although the strategy has various advantages, it presents several drawbacks mainly because the low numerosity of such populations, the potential selection bias leading to the under-representation of specific migrant groups, and the variety of sampling frames, often depending on the differentiated availability and composition of population lists across contexts. The latter becomes particularly relevant in comparative research. Moving from these considerations, the session welcomes papers focusing on strategies aimed at including subsamples of migrants in general population samples. Both contributions dealing with specific techniques (i.e., oversampling techniques, onomastic procedures, focused enumeration) or practical experiences will be appreciated. In addition, also contributions reflecting on the pros and cons of using immigrant sub-samples from already existent survey programs (i.e., the European Social Survey, the European Values Study or the European Union Labour Force Survey) are welcomed.
Keywords: survey, migrants
Dr Raffaele Vacca (University of Milan) - Presenting Author
Dr Basak Bilecen (University of Groningen)
Personal social networks play a crucial role in migration and migrant incorporation in different domains of social life. Studies of these networks in specific immigrant communities have grown in number in recent years, yet comparisons of migrants’ personal networks with those of non-migrants are still rare. This type of comparisons could be key to understanding inequalities, disadvantages, and resources linked to migration. This paper presents a comprehensive and systematic comparison of personal networks between three migration status groups -- first-generation migrants, migrant descendants (the "second generation"), and individuals with no migration background -- in the San Francisco Bay (SFB) area of California (USA). We use longitudinal data from the UCNets study, an innovative, general-population panel survey on social networks and health. Conducted in 3 waves (2015-2018), UCNets provides uniquely extensive and detailed information on respondents' personal networks, health behaviors and outcomes, and life events. While the study's sampling and recruitment procedures did not specifically target individuals with migration background, first-generation and second-generation migrants represented approximately 20% and 10% of respondents in Wave 1, respectively, due to the high ethnic diversity levels of the SFB general population. We find that the size of personal networks varies significantly between migration groups, even when controlling for other sociodemographic characteristics. The average first-generation migrant has a significantly smaller network and more limited access to social support in all domains – including social companionship, emotional support, and everyday practical support – compared to non-migrants and the second generation. As expected, first-generation migrants also have more geographically dispersed networks. There is little evidence, however, that migrants’ networks are characterized by higher prevalence of difficult relationships and strong ties, contrary to certain descriptions in migration literature. Family and household structures are also similar across the three groups.
Mr Giorgio Piccitto (University of Catania) - Presenting Author
Recently the study of the children of immigrants in Europe is gaining momentum and broadening its range and scope (Heat et al., 2008). This group has to face the ethnic penalty, namely the gap between immigrants’ children and natives in labor market achievement after having accounted for the individual characteristics. While the literature has studied this issue in country which historically were destination of migration flows, little is known on ‘new’ receiving countries like Italy (Gabrielli and Impicciatore, 2022). Italy represents a theoretically interesting case study: it is characterized by a weak socio-economic integration of migrants, which have similar (or higher) chances to be employed than natives, due to their availability to fill the lower strata of the occupational hierarchy, but they are remarkably penalized in terms of the socio-economic status of their job. Since Italy is a country which only recently has surged as important destination of migration flows, the quota of immigrants’ children active into the labor market is significantly lower that of the old migration countries, and this prevents to provide an assessment of their performance in the labor market. In this work, we aim at filling this gap by exploiting a particularly fortunate source of data represented by two Eurostat “ad hoc modules” of the 2008 and 2014 European Union Labour Force Survey (EU-LFS). Applying multivariate statistical techniques to these data, we are able to (provisionally) shed light on the labor market situation of the immigrants’ children.
Ms Katrin Pfündel (Federal Office for Migration and Refugees Germany) - Presenting Author
Dr Anja Stichs (Federal Office for Migration and Refugees)
Surveys addressing immigrants usually lack an appropriate sampling frame, because public statistics are missing or unavailable. Sampling procedures for rare populations, such as the increasingly popular onomastic sampling technique can help. General population surveys can profit from the experiences within immigrant-specific surveys applying this technique such as the study Muslim Life in Germany, conducted by the Research Centre of the Federal Office for Migration and Refugees. It includes subsamples of persons with a migration background from Muslim-majority regions of origin. We use an innovative two-step procedure to establish a sampling frame for this population. First, we generate a representative national sample by selecting roughly 300 local resident registration offices. Second, we identify names, which match our specific study population using onomastics. Notice that our sample is not restricted to any place of birth or religious affiliation.
Even though several case studies exist that sample specific immigrant populations by the onomastic procedure, little is known about the reliability of this approach regarding the differentiation of immigrants with similar names. This paper contributes to the literature by evaluating the reliability of onomastics for similar populations, in other words, immigrants from five Muslim-majority regions (Turkey, Southeast Europe, Asia, Middle East, and Northern Africa). Subsequently, we describe the implementation of the method and evaluate the realised sample: we compare characteristics of target persons whose names have been correctly assigned to a specific region of origin group with characteristics of non-target persons who were falsely assigned to a group. In addition, we are able to examine the accuracy of the onomastic procedure for Muslim individuals from the aforementioned origin groups. For our analyses, we use different data sources: nationalities recorded by the resident registration offices, name-based classifications from onomastics, and various information from the survey and screening data set.