Christoph Spörlein, Cornelia Kristen, Regine Schmidt, Jörg Welker, Selectivity profiles of recently arrived refugees and labour migrants in Germany in:

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SozW, Volume 71 (2020), Issue 1-2, ISSN: 0038-6073, ISSN online: 0038-6073, https://doi.org/10.5771/0038-6073-2020-1-2-54

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Christoph Spörlein*, Cornelia Kristen**, Regine Schmidt*** and Jörg Welker**** Selectivity profiles of recently arrived refugees and labour migrants in Germany Abstract: Migrant selectivity refers to the idea that immigrants differ in certain characteristics from individuals who stay behind in their country of origin. In this article, we describe the selectivity profiles of recent migrants to Germany with respect to educational attainment, age and sex. We illustrate how refugees differ from labour migrants, and we compare the profiles of Syrian refugees who successfully completed the long journey to Europe to Syrian refugees who settled in neighbouring Lebanon or Jordan. We rely on destination-country data from the IAB-BAMF- GSOEP Survey of Refugees, the Arab Barometer, and the German Microcensus, as well as on a broad range of origin-country data sources. Regarding sex selectivity, males dominate among refugees in Germany, while among economic migrants, sex distributions are more balanced. Relative to their societies of origin, labour migrants are younger than refugees. At the same time, both types of migrants are drawn from the younger segments of their origin populations. In terms of educational attainment, many refugees compare rather poorly with average Germans’ attainment, but well when compared to their origin populations. The educational profiles for labour migrants are mixed. Finally, Syrians who settle in Germany are younger, more likely to be male and relatively better educated than Syrians migrating to Jordan or Lebanon. Keywords: Refugees; Labour Migrants; New Immigrants; Educational Selectivity; Age Selectivity; Sex Selectivity; Germany Selektivitätsprofile neu zugewanderter Geflüchteter und Arbeitsmigranten in Deutschland Zusammenfassung: Migranten unterscheiden sich in bestimmten Merkmalen von Personen, die im Herkunftsland verbleiben. Der vorliegende Beitrag widmet sich der Beschreibung dieser Selektivitätsprofile. Das Augenmerk richtet sich auf die * Christoph Spörlein, Department of Social Sciences, Heinrich Heine University Düsseldorf, Ulenbergstraße 127, 40225 Düsseldorf, christoph.spoerlein@uni-duesseldorf.de. ** Cornelia Kristen, Chair of Sociology, area Social Stratification, University of Bamberg, Feldkirchenstraße 21, 96052 Bamberg, E-Mail: cornelia.kristen@uni-bamberg.de. *** Regine Schmidt, Chair of Sociology, area Social Stratification, University of Bamberg, Feldkirchenstraße 21, 96052 Bamberg, E-Mail: regine.schmidt@uni-bamberg.de. **** Jörg Welker, Leibniz Institute for Educational Trajectories (LIfBi), Wilhelmsplatz 3, 96047 Bamberg, E-Mail: joerg.welker@lifbi.de. SozW, 71 (1-2) 2020, 54 – 89 DOI: 10.5771/0038-6073-2020-1-2-54 Charakteristiken Bildung, Alter und Geschlecht. Einerseits wird untersucht, welche Unterschiede zwischen Geflüchteten und Arbeitsmigranten zu beobachten sind; andererseits werden syrische Geflüchtete, welche den weiten Weg nach Europa auf sich genommen haben, mit syrischen Geflüchteten verglichen, die in die benachbarten Länder Libanon oder Jordanien gewandert sind. Die Analysen stützen sich auf zwei Datensätze zu Neuzuwanderern in Deutschland, die IAB-BAMF-SOEP Befragung von Geflüchteten und den Mikrozensus, sowie auf den Arab Barometer, der Informationen zu Syrern in Jordanien und dem Libanon beinhaltet. Außerdem wird eine Vielzahl von Datenquellen aus den jeweiligen Herkunftsländern genutzt. Während unter Geflüchteten in Deutschland vor allem Männer vertreten sind, erweist sich die Geschlechterverteilung unter Arbeitsmigranten als ausgeglichener. Darüber hinaus sind Arbeitsmigranten, verglichen mit der Bevölkerung im jeweiligen Herkunftsland, jünger als Geflüchtete. Gleichzeitig zeigt sich für Arbeitsmigranten und Geflüchtete gleichermaßen, dass sie zu den jüngeren Segmenten ihrer jeweiligen Herkunftsgesellschaft gehören. Hinsichtlich der Bildungsselektivität lässt sich festhalten, dass Geflüchtete im Vergleich zur deutschen Bevölkerung zwar zumeist schlechter gebildet sind, dass sie jedoch im Vergleich zur Herkunftsbevölkerung einen höheren Bildungsstand aufweisen. Die Bildungsprofile von Arbeitsmigranten erweisen sich dagegen als heterogen. Syrische Geflüchtete, die sich in Deutschland niedergelassen haben, sind jünger, häufiger männlich und vergleichsweise besser gebildet als Syrer, die nach Jordanien oder in den Libanon gewandert sind. Schlüsselworte: Geflüchtete; Arbeitsmigranten; Neuzuwanderer; Bildungsselektivität; Altersselektivität; Selektivität nach Geschlecht; Deutschland Introduction In a prominent contribution, Lee (1966: 56) addressed migrant selectivity by claiming that immigrants are “not a random sample of the population at origin”, but differ in certain characteristics from their non-migrating counterparts who stay behind. Selectivity can manifest in a variety of attributes. Pioneer migrants, for example, are often male (e.g., Lindstrom/López Ramírez 2010), and despite increasing numbers of female migrants, less-developed countries still see a higher proportion of men migrate than women (Gosh 2009). Compared to those who remain in the country of origin, migrants are usually younger (e.g., Birchall 2016; Lindstrom/ López Ramírez 2010) and healthier (e.g., Kennedy et al. 2015; Lu 2008; Ro et al. 2016; Weeks et al. 1999). Migration scholars, in addition, have argued that immigrants are among the most ambitious, motivated and risk-tolerant members of their home country population (e.g., Bonin et al. 2006; Chiswick 1978; Polavieja et al. 2018; Portes/Rumbaut 1996). 1 Selectivity profiles of recently arrived refugees and labour migrants in Germany 55 In sociology and economics, the probably most frequently considered selectivity attribute is educational attainment (e.g., Borjas 1987; Chiswick 1999; Ichou 2014; Feliciano 2005). Educational selectivity has been shown to be relevant for immigrants’ integration prospects along various dimensions. Recent empirical studies mostly point to favourable consequences of being positively selected on education, for example, for immigrants’ labour market performance (Picot et al. 2016), for their health (Ichou/Wallace 2019), for the pace of destination-language acquisition (Spörlein/Kristen 2018) or for the second generation’s educational attainment (Feliciano 2005; Feliciano/Lanuza 2017; Ichou 2014; van de Werfhorst/Heath 2018). Educational selectivity in these contributions is seen as indicative of other, usually unmeasured attributes such as a person’s motivation or drive to succeed, cognitive skills or access to further resources (Engzell 2019; Feliciano 2005; Ichou 2014; Spörlein/Kristen 2019). In this article, we aim at describing the selectivity profiles of recent immigrants with respect to three attributes: sex, age and educational attainment. Descriptions of the sex and age composition provide information on the general makeup of the contemporary migrant population. The central focus, though, is on educational attainment, as this is the characteristic usually considered when addressing selectivity differences between different types of migrants, and because it has been linked to immigrants’ and their children’s incorporation into host societies. In our description, using the IAB-BAMF-GSOEP Survey of Refugees in Germany, we consider on the one hand refugees who recently came to Germany from Afghanistan, Eritrea, Iraq and Syria (Brücker et al. 2016; Kroh et al. 2016). Many of these immigrants left their home countries in times of war and violent conflict. In this regard, they differ from other migrants, such as economic migrants, who leave their countries of origin for other purposes. Even though Germany is only one of many European destinations, it is by far the largest recipient, having received 46 percent of the total refugee population that headed for Europe and settled there between 2014 and 2018 (Eurostat 2019). On the other hand, we study how recent refugees compare to other new immigrants who came to Germany for different reasons, as is the case of labour migrants and individuals migrating for family matters or educational purposes. Since the German Microcensus (GMC), on which we base our analysis, does not allow a distinction to be made between economic and other types of migrants, we treat them together and subsume them under the term “labour migrants”. We opt for this route because there are empirical indications which suggest that in most migrant groups under study, labour migrants make up the largest share. At the same time, we recognize that the assumptions made for economic migrants do not always apply to other kinds of migrants. We will get back to this issue when describing the different data sources. 56 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Moreover, we compare the selectivity profiles of Syrian refugees who successfully overcame the long journey to Germany to those of Syrian refugees who migrated to neighbouring Jordan and Lebanon. As will be discussed below in more detail, the longer distance to Europe could result in a more positive human capital selection of Syrian refugees coming to Germany compared to those settling in Jordan or Lebanon. We also expect Syrians in Germany to be younger and more likely to be male than Syrians in the adjacent countries. For the description of Syrian refugees in Jordan and Lebanon, we use the Arab Barometer Wave IV (AB IV; AlKhatib et al. 2016). The motive for migrating has been a recurring issue in debates about whether immigrants are typically drawn from the upper rather than the lower echelons of the educational attainment distribution (Chiswick 1999; Feliciano 2005). The arguments brought forward in this context refer to conditions that are assumed to differ between refugees and economic migrants. In the theoretical part, we consider these arguments and link the respective reasoning to a general account of migratory behaviour. In this way, we aim at illustrating how systematic variation in individual decisions about staying versus leaving, as well as about choosing specific destinations, may generate distinct selectivity profiles for different kinds of migrants. For our empirical descriptions, we move away from group-based definitions of selectivity towards a definition at the individual level. Ichou (2014) introduced a measure that pinpoints the individual migrant’s relative position in the distribution of a certain characteristic in a population. This individual-level perspective acknowledges that origin groups can consist of varying shares of differentially selected individuals. In fact, empirical findings confirm that migrant groups are often composed of individuals that spread over the whole selectivity spectrum, rather than of individuals concentrating on one end, or around a certain value on that spectrum (Spörlein/Kristen 2019). In our empirical study, we consider migrant selectivity both relative to the population in the origin country and relative to the majority population in the destination country. Both types of comparisons are meaningful when assessing immigrants’ integration prospects. The former informs us about the relative value a certain qualification has in the context of the origin country. This value varies with the prevalence of the qualification in question. For example, in a society, “in which the average level of education is lower, a medium level educational degree is relatively more valuable than it is in a context in which the average level of education is higher and where most individuals complete at least a medium degree” (Spörlein/Kristen 2019: 4). Hence, immigrants who do not have a high level of education according to the destination country standards may nonetheless be positively selected relative to the general population in their home countries (Lieberson 1980: 214). The latter comparison to the majority population in the destination country, in contrast, provides us with information about immigrants’ relative standing in the receiving society. Selectivity profiles of recently arrived refugees and labour migrants in Germany 57 This location might be of special relevance in a destination country such as Germany, where educational qualifications and certificates are particularly important for individuals’ labour market prospects (Bol/van de Werfhorst 2011; Breen 2005), and where ethnic inequality in the labour market is more pronounced than in other European countries (Lancee 2016; Spörlein 2018). The empirical analyses rely on data on recent refugee and labour migrants in their destination countries, as well as on the populations in their respective countries of origin. Accordingly, we use a range of data sources that allow different types of immigrants (i.e., refugees and labour migrants in Germany, as well as Syrian refugees in Jordan and Lebanon), the reference populations in their origin and destination countries, and the selectivity characteristics of interest (i.e., sex, age and educational attainment) to be considered. The various comparisons will provide us with a differentiated characterization of current migration flows. Selectivity among refugees and labour migrants The notion that the selectivity profiles of refugees differ from those of labour migrants has been discussed in the context of statements that centre on the motive for migrating (Chiswick 1999; Feliciano 2005). The arguments brought forward in this context can be linked to a general account of migratory behaviour that follows applications of the human capital model (Becker 1964; Chiswick 1999, Sjastaad 1962) and of value-expectancy theory (De Jong et al. 1983; Kalter 2000). The general idea is that individuals consider the costs and benefits of leaving versus staying, as well as the probabilities that selecting one of these alternatives will bring about the expected returns. They eventually choose the alternative that is perceived as most promising. In the following, we discuss the various arguments raised in the literature in relation to this theoretical framework and illustrate their application to contemporary refugees. We start by considering the decision about whether to migrate and, subsequently, address the decision about where to migrate (Massey et al. 1998; Kalter 2000). Both are relevant for an account of migrant selectivity. The migration decision and migrant selectivity Much of the reasoning about why individuals decide to leave the place where they grew up centre on the costs and benefits associated with staying versus leaving. Benefits encompass all those incentives that individuals perceive as an improvement upon their current life situation such as obtaining a more favourable position in the labour market, a higher income or better chances for their offspring. They point to the so-called “pull factors” of migration (Lee 1966). Conversely, it is also necessary to consider conditions pushing individuals to leave such as high unemployment, and poor labour market prospects in the country of origin or, as in the case of violent conflict and war, physical harm. In these instances, migration can alleviate the strains that “push factors” impose on individuals’ lives. 2 2.1 58 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker The perceived benefits of migrating are contrasted with the costs that migration incurs. These costs include the direct expense of travelling to the destination, as well as less tangible costs such as the psychological burden of losing social networks, or potential difficulties associated with settling in a culturally dissimilar context (Borjas 1987; Jasso/Rosenzweig 1990; Massey 2010). Finally, migration can be a risky endeavour, and leaving may not necessarily mean that the destination is reached or that the expected benefits can be realized in the host society. For example, migrants may not be able to find a suitable job and thus fail to achieve their aims. In the case of refugees to Europe, the dangerous nature of migration is illustrated by the large number of drownings in the Mediterranean Sea. In the following, we take up this general reasoning about migratory behaviour and apply it to recent refugees and labour migrants in Germany. We consider arguments that address the question of why the selectivity profiles of refugees might differ from those of economic migrants. In essence, most of the answers prominently discussed in the literature boil down to the motive for migrating, and they mostly are connected with educational selectivity. While labour migrants voluntarily choose to leave, the experience of war and violent conflict typical for contemporary refugee populations create conditions that impose considerations on individuals who otherwise may not have seriously contemplated migration. Put differently, the key issue seems to be whether the decision to go elsewhere is motivation-based, and hence driven largely by pull factors, or whether it is based on external danger or threat, and thus on circumstances that push individuals out of their place of origin. This reasoning does not preclude the fact that refugees, in addition to push factors, consider pull factors and, vice versa, that labour migrants, in addition to pull factors, consider push factors. Lee (1966: 56), in this context, argues that migrants, who base their migration decision largely on what he calls “plus factors” at destination (i.e., pull factors), tend to be positively selected compared to those who stay behind. They are under no need to go elsewhere but do so because they perceive opportunity in the destination country and have weighed the advantages and disadvantages of staying versus leaving. Consequently, economic migrants should be relatively better educated than the population in the country of origin. In contrast, individuals who respond primarily to “minus factors” at origin (i.e., push factors) tend to be negatively selected. This line of reasoning is frequently echoed in the literature (e.g., Chiswick 1999; Feliciano 2005; Grogger/Hanson 2011; Lessard-Phillips et al. 2014). Accordingly, recent labour migrants settling in Germany should be more favourably selected on education than refugees from Afghanistan, Eritrea, Iraq and Syria. Regarding age selectivity, the human capital model suggests that younger individuals are more likely to migrate as they have more time left in which they can be active in the labour market and realize returns on their migration-related investment (Becker Selectivity profiles of recently arrived refugees and labour migrants in Germany 59 1964, Chiswick 1999). In addition, younger individuals tend to be healthier and are therefore better able to cope with the strains associated with migration. Younger people are also more likely to be single and not have children, allowing them to be more flexible. The general notion, therefore, is that migrants are disproportionally drawn from the younger segments of the origin population (Birchall 2016; Lindstrom/López Ramírez 2010). This reasoning applies in particular to labour migrants who base their migration decision on pull factors. In contrast, conflict and war can force substantial proportions of populations living in afflicted regions to go elsewhere. In these instances, the age dispersion among refugees should be greater, suggesting that younger individuals will not dominate the picture in the same way as they do in other kinds of migration flows. In extreme cases, when everyone has to leave, refugees will not be selected on age at all (or on any other attribute; Lee 1966: 56). Relative to their societies of origin, contemporary refugees should thus be older than labour migrants. Regarding sex selectivity, the arguments raised touch upon the gender-specific division of labour. In many societies around the world, women are still mostly in charge of family matters, while as breadwinners men are expected to be active in the labour market and support their families financially. Accordingly, males are more likely to migrate than females, for whom migration can be more costly given the inflexibilities associated with having and taking care of children (Stock 2012). Sex imbalances in favour of males are expected to vary according to the extent to which genderspecific norms and expectations are enshrined in the society of origin. While men are expected to dominate the migration streams from more traditional societies, a more balanced picture should emerge for the migration streams from modern societies. Because contemporary refugees mostly stem from traditional societies, genderspecific norms together with societal beliefs about whether it is acceptable for women to travel on their own (Birchall 2016) may thus contribute to a composition in favour of males. However, war and imminent threat may counteract these forces. In instances where large segments of the population are pressured to leave, females and children will also migrate, though, as we will argue below, they will tend to do so to a place nearby rather than to a distant destination. Destination choice and migrant selectivity Migration also involves the choice of a certain destination. Once again, individuals can be assumed to consider different alternatives, such as leaving for a distant country in Europe, or ending their journeys in a place close by. Migrants decide between these destinations based on assessments of the costs and benefits attached to the different alternatives, as well as of the probabilities of being able to reach each of the destinations and realize the corresponding returns. In the following, we apply this reasoning to recent Syrian refugees and argue for differences in the selectivity profiles of Syrians leaving for Europe versus Syrians lea- 2.2 60 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker ving for destinations closer to home. Important destinations for individuals who fled Syria between 2012 and 2018 have been the neighbouring states of Iraq, Jordan, Lebanon, and Turkey. In 2018, about 5.5 million refugees were registered in these countries (UNHCR 2019).1 Of all the Syrians who arrived in Europe between 2014 and 2018, 56 percent came to Germany (Eurostat 2019). In 2018, 714,645 Syrian nationals were listed as living there (Statistisches Bundesamt 2019). Reaching a distant destination in Europe takes longer, and in many cases is more dangerous compared to settling in a state nearby. Therefore, traveling to Germany should be associated with greater costs as well as a higher risk of not making it there safely. Covering the financial expenses of a trip to Europe requires substantial funds (Brücker et al. 2016: 28), with the more educated having more resources at their disposal to cover these costs (e.g., for shelter or traveling). Moreover, in light of Germany’s more prosperous economic situation, migrants might perceive the benefits of heading to this destination to be greater than the benefits of settling in one of their country’s less affluent neighbour states (Brücker et al. 2016: 28). Assuming that individuals who have acquired more education are better prepared to realize the benefits associated with migrating to a distant location, and therefore anticipate higher returns for their education, they are expected to opt for this alternative more often than individuals with lower levels of education. Accordingly, in terms of educational selectivity, Syrian refugees in Germany should be more favourably selected on education than Syrian refugees in states contiguous to Syria. The reasoning on age selectivity directly links to the arguments outlined above. That is, younger people have more time left in which they can realize the returns on their migration-related investments. They should also be healthier and more able to cope with the often difficult and strenuous traveling conditions associated with journeying to a place farther away. Consequently, Syrian refugees heading to Germany should be younger than those entering one of their neighbouring countries. Finally, in terms of sex selectivity, we expect pronounced differences in the sex composition of refugees to Europe versus those entering a neighbouring country – with males dominating the migration stream to Germany, and a more balanced picture emerging for states adjacent to Syria. Since in many cases the journey to Europe involves danger, and given that women typically are more vulnerable to some threats (e.g., sexual assault), assessments of the likelihood of making it safely to the destination country may differ between females and males. As a result, females are expected to head for a closer destination. In addition, women’s family obligations may render migration to a neighbouring country more attractive, as it is easier for a 1 Iraq: n=251,157 Syrian refugees, 0.6 percent of the total population of Iraq; Jordan: n=667,186 Syrian refugees, 6.7 percent of the total population of Jordan, Lebanon: n=968,083 Syrian refugees, 15.9 percent of the total population of Lebanon; Turkey: n=3,562,523 Syrian refugees, 4.4 percent of the total population of Turkey (for the populations of Syrian refugees in 2018, see UNHCR 2019; for the estimates of the population sizes in 2018, see United Nations, Department of Economic and Social Affairs, Population Division 2017). Selectivity profiles of recently arrived refugees and labour migrants in Germany 61 family or for several people to travel there together (Stock 2012). It may also be less acceptable in certain contexts for females to move or travel on their own, so that if women have to leave, they may travel only shorter distances (Birchall 2016). Taken together, we expect Syrians who settle in Germany to be younger, more likely to be male, and relatively better educated than Syrians migrating to Jordan or Lebanon. Measuring selectivity The notion of immigrant selectivity applies to a variety of different characteristics. Sometimes these features can be directly observed, as is the case with education, age or sex. At other times, it is unobserved (or unobservable) attributes that may be of interest to research. In these instances, researchers rely on observable characteristics in the hope of approximating the distributions of the underlying unobserved traits. For example, educational attainment is seen as indicative of latent, oftentimes unmeasured attributes such as immigrants’ motivation levels, their drive to succeed or cognitive competences (Engzell 2019; Feliciano 2005; Ichou 2014; Spörlein/ Kristen 2019). Thus our measurement of educational selectivity, consequently, captures both observable and unobservable characteristics. In prior research, selectivity has been measured in a number of different ways. Most can be grouped along two conceptual axes: the level of analysis (i.e., individual-level versus group-level), and the reference population (i.e., the population of the origin country versus the population of the destination country). Until recently, group-based definitions of migrant selectivity have been the main route for assessing the extent of selectivity, and of analysing the relationship between selectivity and immigrants’ integration. Two approaches of group-level definitions stand out. The first approach relies on macro-level characteristics of the country of origin and/or destination. Empirical contributions, for instance, consider the geographic distance between the origin and the destination country (e.g., Spörlein/van Tubergen 2014; van Tubergen et al. 2004), net earning differentials between migrants and majority members in the destination country (e.g., Borjas 1987), or crosscountry differences in the level of economic development (e.g., Cobb-Clark 1993; Levels et al. 2008). This approach is indirect in that it does not capture the selectivity attribute being investigated. Instead, it is usually argued that the macro-level characteristic is indicative for a specific selection of the migrant population. For example, a larger geographic distance implies greater migration costs, which the more affluent segments of the population are better able to cover. Therefore, a greater distance should be associated with a more positive selection on relevant resources. 3 62 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker The second group-level approach uses information on the selectivity characteristic of interest for the origin population and for the migrants from this country. Applied to educational selectivity, the index of net difference (Lieberson 1980) summarizes the share of migrants who are more educated than non-migrants of the same agecohort, the share of migrants who are less educated, and the share of migrants with levels of education equal to the non-migrant population of the same age in the origin country. Put differently, the index will reveal how often the educational level of a migrant from a certain origin country will exceed that of a non-migrant (e.g., Feliciano 2005; Lessard-Philips et al. 2014; van de Werfhorst/Heath 2018). Both approaches treat origin groups as monolithic blocks and assign the same selectivity value to each migrant of the same origin. An obvious drawback of a grouplevel definition is that the characterization of a group as a whole as either positively or negatively selected is usually unable to capture the empirical distribution. That is, migrant groups are usually composed of positively and negatively selected individuals (Spörlein/Kristen 2019). Individual-level measurements of migrant selectivity respond to this drawback of group-level approaches, and specify how individual migrants compare to a reference population. A more recent rather influential approach which so far has mainly been applied to educational selectivity, positions the individual migrant in the sex- and age-specific educational attainment distribution of the origin country (Ichou 2014). It thus allows for variation in the selectivity values of immigrants of the same origin. By considering sex-specific distributions, the measure takes into account differences between females and males in their access to educational institutions in the origin country; by considering age-specific distributions, it allows changes to be incorporated that are consequence of the educational expansion and, associated therewith, changes in the relative positional value of educational credentials over time. This selectivity measure is also close to the original notion of immigrant selectivity by recording how migrants compare to those who stay in the country of origin (Lee 1966). Another individual-level approach that is usually not explicitly framed as a selectivity measure deals with to migrant selectivity in reference to the distribution of a certain characteristic in the population of the destination country. For example, it has been argued that members of particular migration flows such as labour migrants coming to Western Europe in the 1960 s and early 1970 s, as well as later on their following family members, were less educated than were the populations in the host societies at the time (Kalter/Granato 2007: 284). Selectivity, in these instances, is framed from the perspective of the destination countries. In terms of measurement, these accounts usually rely on comparisons of the absolute educational attainments of immigrants and majority population members. The question of which measurement approach is most suitable certainly depends on what researchers aim to investigate. For the descriptions envisaged in our study, Selectivity profiles of recently arrived refugees and labour migrants in Germany 63 relative measures that locate the individual migrant within the appropriate distribution of a certain characteristic in a reference population are of key importance. In the following, we consider both: how immigrants of different origins compare to those who remain in the origin country and how they compare to the German (and for Syrians, also how they compare to the Lebanese and Jordanian) majority population(s). This last comparison provides an important addition to the overall description of the composition of contemporary migrants, as educational degrees are usually assessed from the perspective of the destination society. For example, on the labour market, immigrants’ educational credentials will not be judged for their relative value in the origin country, but for what these credentials mean in the destination country. In modern Western societies, in which educational expansion is well-advanced, low and medium levels of education, despite their possibly greater value in a different societal context, generally are insufficient for gaining access to higher positions, and therefore limit what immigrants can eventually achieve. Data and selectivity measures Origin- and destination-country data sources For our descriptions of recent immigrants’ selectivity profiles, we rely on a range of origin- and destination-country data sources. For refugees in the destination countries, we consider two data sets. The first is the IAB-BAMF-GSOEP Survey of Refugees in Germany (Brücker et al. 2016; Kroh et al. 2016). It contains data from roughly 4,500 individuals aged 18 and above who arrived in Germany between 2013 and 2016. It is based on a random sample of the Central Register of Foreigners (AZR) with an oversampling of refugee groups who at the time were assumed to have a high likelihood of staying in Germany (such as Afghans, Iraqis and Syrians). In addition, women and individuals older than 30 were oversampled. The analyses using this data set are therefore based on weighted data. We consider only groups with at least 200 respondents and, accordingly, present findings for refugees from Afghanistan, Eritrea, Iraq and Syria. For the Jordanian and Lebanese samples of Syrian refugees, we rely on the Arab Barometer Wave IV (AB IV). The data was collected in 2016 and 2017 and, for each country, provides information on 300 Syrian refugees aged 18 and above. The surveys are based on probability samples of Syrians living among the general population, mainly outside of refugee camps (AlKhatib et al. 2016: 5-6; Arab Barometer Wave IV Technical Report, n. d.). We restricted the sample to individuals who resided in the two countries no longer than five years. The analyses of labour migrants and their accompanying family members are based on the German Microcensus (GMC). The Microcensus is a yearly 1 percent household sample, in which respondents are obliged to participate. We pooled data from 7 years, covering the period from 2008 to 2014. Since we are interested in recent 4 4.1 64 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker immigrants, we restrict the duration of stay to a maximum of five years. Moreover, we use information on nationality to distinguish between different origin groups. Similar to our proceeding for the refugee populations, we only consider migrant groups with at least 200 respondents. In total, our GMC sample covers 21 origin groups ranging from smaller groups (e.g., from Kazakhstan or Thailand) to larger, more well-established groups (e.g., from Poland, Russia or Turkey). Table 1: Data sources and sample sizes Migrant group Destination country Origin country Data and sample size Percent refugees1 Data2 Year Refugees IAB-BAMF-GSOEP Survey of Refugees (Germany) Afghanistan 474 MICS 2011 Eritrea 205 EPHS 2010 Iraq 534 MICS 2011 Syria 2,065 MICS 2006 AB IV (Jordan, Lebanon) 253 245 Labour migrants German Microcensus (GMC) OECD, Eurostat Austria 738 ~ 0 UNdata 2011 Bulgaria 878 0.4 UNdata 2011 China 1,028 3.7 UNdata 2010 Croatia 304 0.2 UNdata 2010 France 768 ~ 0 IPUMS 2011 Greece 561 ~ 0 UNdata 2011 Hungary 832 ~ 0 UNdata 2011 Italy 867 ~ 0 IPUMS 2011 Kazakhstan 257 1.5 UNdata 2010 Morocco 411 9.1 UNdata 2010 Netherlands 600 ~ 0 IPUMS 2011 Poland 4,454 ~ 0 UNdata 2011 Portugal 272 ~ 0 UNdata 2011 Romania 1,541 ~ 0 UNdata 2011 Russia 1,747 14.7 UNdata 2010 Spain 538 ~ 0 UNdata 2011 Thailand 220 ~ 0 UNdata 2010 Turkey 1,987 7.4 UNdata 2011 United Kingdom 516 ~ 0 UNdata 2011 Selectivity profiles of recently arrived refugees and labour migrants in Germany 65 Migrant group Destination country Origin country Data and sample size Percent refugees1 Data2 Year United States 625 ~ 0 IPUMS 2010 Ukraine 513 2.7 UNdata 2001 Note: 1 The numbers provide an estimate of the percentage of refugees that are contained in each origin group. The percentages are derived by dividing the total number of first time asylum applicants by the total number of immigrants for the period between 2003 and 2014. Data sources are the OECD’s International Migration Database: https://stats.oecd.org/Index.aspx?DataSetCode= MIG and Eurostat’s Asylum and Managed Migration Database: https://ec.europa.eu/eurostat/we b/asylum-and-managed-migration/data/database. 2 EPHS (Eritrea Population and Health Survey): https://www.afro.who.int/publications/eritrea-po pulation-and-health-survey-2010, MICS (UNICEF Multiple Indicator Cluster Survey): http://mics.un icef.org/surveys, IPUMS (Integrated Public Use Microdata Series International): https://internation al.ipums.org/international/, UNdata (United Nations database): http://data.un.org/. All destination samples are restricted to individuals aged 15 to 64. Table 1 depicts the different refugee and labour migrant groups and the available sample sizes. It also illustrates that some of the groups that we consider as labour migrants also contain refugees. The German Microcensus does not allow refugees and labour migrants to be distinguished from one another. Therefore, we rely on other data sources such as the OECD International Migration Database and Eurostat’s Asylum and Managed Migration Database to obtain at least a rough estimate of the refugee populations that might be included in our labour migrant sample. As Table 1 indicates, in most cases, refugees make up only a small minority. Exceptions are Moroccans (with 9 percent refugees), Russians (with 15 percent) and Turks (with 7 percent). We decided against excluding these groups, but we keep this limitation in mind when interpreting the results. We only dropped Vietnamese migrants for whom the percentage of refugees amounts to 23. Moreover, the Microcensus does not allow a distinction to be made between economic migrants, family migrants and students. Only in 2014, did it include an additional question on this issue. However, the number of cases of new immigrants available for each origin group in this year is far too small (i.e., for 17 of the 21 migrant groups below 35 cases) for a sound assessment of the shares of different types of migrants (and other data sources do not provide large enough numbers of recent immigrants either). The distributions based on these small numbers at least suggest that migrating for economic reasons is the main motive for most origin groups. For comparisons of recent immigrants with the majority populations in the destination countries, we use the German Microcensus for Germany and the Arab Barometer Wave IV for Jordan and Lebanon. To depict the distributions of educational attainment, sex and age for the populations in the origin countries, we use four different data sources. The first data set, the 66 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Integrated Public Use Microdata Series International (IPUMS), provides census data for a wide range of countries. Second, we rely on UNICEF’s international household survey initiative, the Multiple Indicator Cluster Surveys (MICS), which also covers a variety of countries. In contrast to these two individual-level sources, the remaining two data sets publish only aggregate distributions. They include the United Nations database (UNdata) and the Eritrea Population and Health Survey 2010 (EPHS2010; National Statistics Office/Fafo AIS 2013: 19-20). Given that the aggregations are based on the characteristics of interests, namely, educational attainment, sex and age, they are well suited for our purposes. Selectivity measures In view of the variety of data sources used in this study, harmonization is an issue. There was no need for modifications for sex, and little for age. The latter is categorized into ten 5-year intervals, where the lowest category covers individuals aged 15 to 19, and the highest those aged 60 to 64. For educational attainment, we rely on a variant of the 1997 International Standard Classification of Education (ISCED) and use the following categories: no education (ISCED 0), primary (ISCED 1), lower secondary (ISCED 2), upper secondary (ISCED 3), post-secondary non-tertiary (ISCED 4) and tertiary (ISCED 5, 6). The IAB-BAMF-GSOEP Survey of Refugees in Germany and the GMC on the destination side, and the IPUMS, MICS and UNdata on the origin data side use the same, or very similar classification schemes that can easily be assigned to these categories. The AB IV for the destinations countries Jordan and Lebanon, however, does not record ISCED 4, which in Syria, at about 6 percent seems to be of some importance. We assume that Syrians in Jordan and Lebanon who acquired a degree that qualifies as ISCED 4 have been downgraded to ISCED 3 in the Arab Barometer. In our analyses of this data set, we may therefore slightly underestimate the degree of selectivity of Syrians. The Eritrean origin data provides information on up to secondary education (≤ISCED 3), but little specification on the precise level attained beyond that. We therefore assigned the residual category “more than secondary” to tertiary education (ISCED 5, 6). Among the Eritrean origin population, 6 percent of males and 2 percent of females fall into this category (National Statistics Office/Fafo AIS 2013: 19-20). In the IAB-BAMF-GSOEP Survey of Refugees in Germany data on Eritrean refugees, 8 percent (n=16 of which 1 respondent was female) of all Eritreans report having attained more than ISCED 3. 12 of these 16 cases completed university, which suggests that our strategy is unlikely to grossly misrepresent the education levels of Eritrean refugees. Using the harmonized data, we construct two types of selectivity measures: the first compares immigrants to their origin population (i.e., the origin-relative measure), while the second compares immigrants to their destination population (i.e., the 4.2 Selectivity profiles of recently arrived refugees and labour migrants in Germany 67 destination-relative measure). We consider these measurements to describe the selectivity profiles with respect to age and educational attainment (Ichou 2014). For the measure of age selectivity, we calculate each individual’s quantile position in the sex-specific age distribution. We follow a similar approach for educational selectivity and specify the individual migrant’s quantile position in the sex- and age-specific educational attainment distribution. Each of the four measures ranges from 0 to 1. A value of 0.6 can be interpreted as an individual being older (or more educated) than 60 percent of the origin (or destination) country population. In the context of relative education, values above 0.5 point to a positive selection, whereas values below 0.5 indicate a negative selection. The notion of “positive” or “negative” seems less suited for a description of age selectivity. As a purely distributional feature, we could apply the distinction in the same manner as for education. Correspondingly, a concentration of younger individuals among new immigrants would be reflected in a negative selection. However, an immigrant composition dominated by younger individuals, is generally perceived as a positive phenomenon, even though this introduces a value assessment to an otherwise neutral distributional account. To avoid this confusion, we refrain from using the positive/negative framing for age. In most populations around the world, there are only small imbalances in the agespecific distributions of females and males (rarely exceeding 1 percentage point). This characterization also applies to the origin and destination populations covered in our study. For this reason, calculating relative distributions in the same way as for education and age is less meaningful. It would yield estimates that are almost identical to reporting the percentages of females or males in each age group for both the origin and the destination country. We therefore restrict our description of sex selectivity to one measure, which for each origin group captures the percentage of female migrants. Table 2 and Table 3 in the Appendix provide information on absolute and relative distributions of sex, age and educational attainment separately for each immigrant group. Results In the following, we visualize the selectivity profiles of refugees and labour migrants with respect to sex, age and educational attainment. With the exception of sex, we present the findings relative to the populations in the origin and the destination country. Sex selectivity Figure 1 illustrates the percentage of female migrants within each origin group. Refugees to Germany cluster at the top of the figure indicating that males dominate the recent migrant flows from these countries. Only between 20 (Eritrea) and 26 (Iraq) percent are female. By contrast, the sex composition of Syrians relocating to 5 5.1 68 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Jordan (Syria JO) and Lebanon (Syria LB) is balanced – with slightly over 50 percent female migrants in both countries. These patterns are in line with the reasoning that women are more vulnerable to the threats typically faced on the long and dangerous journey to Europe. Their families may be reluctant to let them take the risk, and may instead concentrate their resources on males whom they perceive as being better prepared to deal with the challenges of migrating to a distant destination (Birchall 2016). Figure 1: Sex selectivity (percent female migrants) Among labour migrants, women make up considerably larger percentages than among refugees. Overall, Figure 1 points to a mostly balanced picture for labour migrants; but there is also variation. On the lower end of the spectrum, women from the United Kingdom are underrepresented at 29 percent. A closer look at the Selectivity profiles of recently arrived refugees and labour migrants in Germany 69 data reveals that many of the men originating from the United Kingdom are highly educated and work in male-typical occupations (e.g., computer scientist). At the other end of the spectrum, the migration flows from Russia, Ukraine and Thailand are dominated by females. In the extreme case of Thai immigrants, only 14 percent are male. The one-sided composition of these origin groups could be related to migration for marital reasons. As statistics on binational marriages in Germany document, women from these three countries frequently marry German men (Nauck 2009). Age selectivity Before discussing the findings on relative age, we consider age in absolute terms for both the different migrant groups who came to Germany, as well as for the populations in the various origin countries. The description for migrants is limited to individuals aged 15 to 64, rather than to the entire populations. Therefore, measures of central tendency such as the median age differ from calculations based on the complete age spectrum.2 Afghan, Eritrean, Iraqi and Syrian refugees to Germany are rather young. Their median age ranges between 26 (Eritrea) and 34 (Syria) (see Table 2 in the Appendix). Despite variation, for the most part the different labour migrant groups are considerably older, with median ages up to 50 (see Table 3 in the Appendix). At the same time, the age structure of the origin populations of refugees on the one hand, and of economic migrants on the other hand, differ substantially. That is, contemporary refugees originate from less developed societies, in which young people make up large shares of the population, while the opposite holds for labour migrants. Economic migrants come from countries with higher levels of economic development, which are characterized by either elderly and shrinking, or stationary populations, and hence feature considerably fewer young people.3 These distributions suggest that, despite refugees being younger in absolute terms than labour migrants, in origin-relative terms (i.e., in comparison to the age distribution of the population at origin), they may not necessarily be younger. 5.2 2 Considering that the extent of this difference varies with the overall age structure of the societies under study, we do not claim to provide a fully accurate picture. Instead, our concern is to illustrate the age difference between recent refugees and labour migrants, and in this way to provide some context for the interpretation of selectivity in terms of relative age. 3 Population Pyramids of the World from 1950 to 2100. Retrieved (for 2013 for all countries under study) from https://www.populationpyramid.net/. 70 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Figure 2: Age selectivity relative to the destination and the origin country Note: The numbers next to each migrant group’s name specify the share of migrants with destination-relative and origin-relative selectivity values greater than 0.5. They indicate the proportions of migrants who are older than half of the population in the reference country. Selectivity profiles of recently arrived refugees and labour migrants in Germany 71 For each migrant group, Figure 2 illustrates the density distributions of age – relative to the origin population (grey) and relative to the destination population (black). At the top, the graph shows the distributions for refugees. Below, it depicts the distributions for the different labour migrant groups that are arranged according to their geographic location. Next to each group’s name, we specify the share of migrants with destination-relative and origin-relative selectivity values greater than 0.5. These numbers inform us about the proportions of migrants who are older than half of the population in the reference country. For example, for the originrelative description of Eritrean refugees, the value of 0.46 indicates that 46 percent of the Eritreans who came to Germany are older than half of the population in Eritrea, while 54 percent are younger. The destination-relative profile, however, reveals a rather different picture with only 21 percent of Eritrean refugees being older than half of the population in Germany. Considering that the Eritrean origin population is very young, while the German population is considerably older, the discrepancy is not surprising. Hence, depending on the point of reference, a rather divergent picture emerges. We now turn to the substantive interpretation of the findings depicted in Figure 2, first focusing on differences in age selectivity between labour migrants and recent refugees, and then between Syrian refugees in different destinations. In a final step, we turn to the destination-relative contrast. Labour migrants are noticeably younger than non-migrants in the origin country. This pattern is reflected in the bulk of age distributions to the left of the 0.5 reference line. Moreover, the proportion crossing the 0.5 reference line rarely exceeds 35 percent, also pointing to age selectivity in favour of younger people. Chinese migrants are exceptional, as only 17 percent are older than half of the population in China. Taken together and in line with the theoretical considerations, recent labour migrants are largely drawn from the younger segments of the population in their origin country – even though they are older than refugees in absolute terms. Refugees to Germany are also selective in age, but not to the same extent as labour migrants. This pattern aligns with the notion that being pushed out in times of war implies less selectivity than in instances where individuals primarily respond to migration pull factors. As argued earlier, when large parts of a population are forced to leave, age selectivity should be less pronounced or, in the extreme, completely disappear. The findings in Figure 2 attest to this. That is, comparing the characteristics of Syrian refugees to Germany with Syrian refugees to Lebanon or Jordan, supports the assumption that refugees who head for a distant destination in Europe are more selective than refugees who leave for a neighbouring country. In fact, Syrians who relocate to one of its adjacent states, tend to be older than the population in Syria. Thus 55 percent of the Syrian migrants in Lebanon, and 64 percent of the Syrian migrants in Jordan are older than half of the population in Syria. Strikingly, Syrian refugees in these destinations are the only two groups under study in which more than half of their members cross the 0.5 threshold. 72 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker The characterization of refugees to Germany as less selective in terms of age compared to labour migrants, changes substantially when focusing on the distributions relative to the destination population. Comparing refugees to the German majority reveals that these migrants are considerably younger. This observation is seen in the placement of the black distributions to the left of the grey distributions. As pointed out above, the majority of Eritrean refugees rank in the bottom quartile of the German age distribution, and only 21 percent cross the 0.5 threshold. This value indicates that 79 percent of the Eritrean refugees in Germany are younger than 50 percent of the German population. Afghans are another good example for this pattern. While they are on average as old as 46 percent of the Afghan population, they are only as old as 25 percent of the German population, suggesting that also this group is composed of considerably younger individuals. For labour migrants, especially for those from Western Europe/North America and Eastern Europe, there are few differences between the relative-origin and the relative-destination age distributions. This result follows from the more similar age structures typical for the origin countries and for Germany. Only for the origin groups from Africa/Middle East and Asia do we find somewhat larger deviations between the origin- and destination-relative age selectivity profiles, but these differences are still small compared to those found for refugees. Educational selectivity As we did for age, we first address education in absolute terms. This step reveals the profound differences between the educational attainment distributions of the societies under study. These disparities might otherwise go unnoticed, as they are not captured in the measures of relative education. Figure 3 presents the various absolute educational attainment distributions separately for each origin group (see also Table 2 and Table 3 in the Appendix). The first six rows depict the percentages in the form of bars for refugees, while the remaining rows refer to the different labour migrant groups. In addition, the bottommost row shows the educational attainment distribution for the German reference population. The distributions for refugees are dominated by bars denoting the lower spectrum of ISCED categories pointing to their rather low levels of absolute education. Between 26 (Syrians) and 56 percent (Afghans) of the refugees in Germany have at most completed primary education (see Table 2 in the Appendix), while in the German reference population, these categories are virtually non-existent (<3 percent). Compared to Syrian migrants in Germany, Syrian migrants in Jordan and Lebanon show a profoundly different educational composition with 42 and 58 percent having acquired no or only primary education, while, at 26 percent, this share is much smaller among Syrian refugees in Germany. 5.3 Selectivity profiles of recently arrived refugees and labour migrants in Germany 73 Figure 3: Absolute educational attainments of refugees, labour migrants, and the German majority In contrast to the refugee populations, only few labour migrants have attained less than secondary education, especially among those hailing from Western Europe/ North America. While being better educated than refugees in absolute terms, the distributions also reflect considerable variation across the various labour migrant groups, in particular regarding tertiary education. For most migrants from Western Europe/ North America, a university degree is typical, with shares of tertiary education at above 40 percent. The corresponding proportions for Eastern Europeans range between 20 and 40 percent. Only Moroccans and Turks (with 10 and 13 percent) range well below all other labour migrant groups. Among refugees, tertiary educational attainment is less common than among labour migrants. At the same time, Figure 3 points to substantive variation within the refugee population. While Iraqis, at 19 percent, and Syrians in Germany at 29 percent, are university-educated, the respective percentages in the remaining groups are relatively small. They range between 4 (Syrians in Lebanon), 8 percent (Eritreans in Germany as well as Syrians in Jordan) and 12 percent (Afghans in Germany). Overall, the educational attainment profiles of refugees show a high prevalence of low levels of absolute education with the modal category being primary education (ISCED 1; 27 percent), while labour migrants on average are more educated. Their modal category is upper secondary education (ISCED 3; 34 percent), directly followed by tertiary education (ISCED 5, 6; 33 percent). 74 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Figure 4: Educational selectivity relative to the destination and the origin country Note: The numbers next to each migrant group’s name specify the share of migrants with destination-relative and origin-relative selectivity values greater than 0.5. They indicate the proportions of positively selected migrants. Selectivity profiles of recently arrived refugees and labour migrants in Germany 75 How do the absolute educational profiles of migrants compare to the profiles of those who stayed behind? To answer this question, Figure 4 depicts the density distributions of migrants’ educational attainment relative to the population in the respective origin country (grey), as well as relative to the population in the destination country (black). The values next to the names of the different migrant groups again denote the proportion of individuals with selectivity values above 0.5. Although lower levels of absolute education are typical for the recent refugee populations, these migrants compare favourably to their compatriots remaining in the origin countries. For example, 75 percent of the Syrians who recently arrived in Germany are positively selected. The Afghan group provides another striking example, with 66 percent being positively selected relative to the origin population. These patterns suggest that even little exposure to education – at least when viewed from the perspective of Western societies where only a minority leaves school with less than secondary education – is enough to generate profiles that reflect a favourable selection. Mirroring the findings for absolute education and in line with the theoretical expectations, Syrian refugees to Germany are more often positively selected on education (75 percent) than Syrians migrating to one of the neighbouring states (59 percent in Jordan and 46 percent in Lebanon). The corresponding picture for labour migrants is mixed and the various groups differ considerably in their degree of educational selectivity. While an overall characterization of labour migrants’ selectivity profiles does not seem adequate in view of this variation, the geographic grouping, at least, allows for a cautious comparison of labour migrants from Western Europe/North America and Eastern Europe. The two remaining categories Africa/Middle East and Asia contain too few groups to be meaningfully included in this comparison. With the exception of recent migrants from Greece, immigrants from Western Europe and North America are mainly composed of positively selected migrants. For example, three quarters of all immigrants from France, Spain and the United Kingdom compare favourably in terms of education levels to those left behind. This selection is driven by immigrants who have acquired tertiary education. Visually, this positive selection is seen in the grey peaks to the right hand side of the vertical line. This pattern sets labour migrants from Western Europe and North America apart from positively selected refugees to Germany, for whom an advantageous position in the origin country’s educational distribution is also driven by lower than tertiary educational attainment. The distributions for Eastern Europeans are indicative of a considerably less selective educational profile compared to Western European and North American immigrants. At the lower end of the spectrum are migrants from Kazakhstan, with 24 percent being positively selected, while at the upper end of the spectrum are immigrants from Romania with 54 percent being positively selected. In addition, the visual illustration of the distributions in Figure 4, shows that the selectivity profiles of Eastern Europeans are more heterogeneous than the profiles of their Western 76 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker European counterparts. That is, they are more equally distributed over the whole selectivity spectrum rather than being concentrated in the upper parts, as is typical for labour migrants from Western Europe. Finally, we consider the additional contrast to the population in the destination country. The key finding for refugees is apparent at a glance: all groups are negatively selected compared to the respective destination population. Assessments of selectivity thus very much differ depending on the reference population considered. While the median Afghan migrant is at least as educated as 68 percent of Afghanistan’s population, the comparison of the median Afghan migrant with Germans yields a value of 2 (see Table 2 in the Appendix). This value indicates that s/he is only as educated as 2 percent of the German population. For Eritrean refugees, a rather similar pattern emerges with a median origin-relative selectivity index of 55 and a median destination-relative index of 3. The findings on Syrian refugees in the different destinations also attest to a characterization of refugees as predominantly negatively selected. The median Syrian migrant is as educated as 14 percent of Germany’s, 11 percent of Jordan’s and 6 percent of Lebanon’s population. For none of the labour migrant groups from Western Europe/North America and Eastern Europe is the difference between origin-relative and destination educational selectivity as pronounced as it is for refugees. Visually, this result is portrayed by largely overlapping grey and black distributions typical for these groups of economic migrants or, conversely, in the greater discrepancies in these distributions among refugees, especially at the lower end of the selectivity spectrum. It also appears in the discrepancy of the proportions of migrants that are positively selected relative to the destination versus the origin country. These differences are large among refugees (between 36 and 46 percentage points), while being much smaller among labour migrants from Western Europe/North America and Eastern Europe (between 1 and 17 percentage points). Only the profiles for migrants from Turkey, Morocco and Thailand show some resemblance to those of recent refugees. Conclusions In this article, we took up the classic notion of migrant selectivity and compared recent immigrants to individuals who remained in their countries of origin. We also compared these migrants to the population at destination and, accordingly, presented a variety of selectivity profiles. Two comparisons were at the centre of our account: we discussed and empirically investigated how contemporary refugees differ from labour migrants and how Syrian refugees who overcame the long journey to Europe compare to Syrian refugees who settled in the neighbouring states of Jordan and Lebanon. Using a variety of data sources on recent migrants to Germany and on the populations in their origin countries, we presented findings on sex, age and educational attainment. 6 Selectivity profiles of recently arrived refugees and labour migrants in Germany 77 One result stands out among the many descriptions: all groups are composed of varying proportions of differentially selected individuals. The variation within groups usually is more pronounced than the variation across groups. In fact, the distributions for most migrant groups cover the whole selectivity spectrum frequently rendering an overall characterization of a migrant group as either concentrating on one end of the spectrum (or as being positively or negatively selected) inadequate. In view of this considerable within-group variation, it may not always be feasible to assess differences between refugees and labour migrants, as well as between the various migrant groups within these two broad categories in a straightforward manner. In the following, we therefore proceed with a cautious discussion of the various findings on recent immigrants in Germany. Three patterns seem to stand out. First, refugee migrants are predominantly male, whereas most labour migrant groups cluster around sex-parity. Second, relative to the societies of origin, migrants are younger than individuals who stay behind; and labour migrants are younger (relative to their origin society) than refugees. Third, in terms of educational attainment, refugees perform rather poorly relative to German standards. Nevertheless, they mostly compare positively to their origin population. For labour migrants, the picture of educational selectivity is more mixed with Western Europeans being overwhelmingly positively selected – also somewhat more favourably than refugees –, whereas the profiles for Eastern Europeans are more heterogeneous and rather indicative of a slight negative selection. However, given the broad variation within these groups, which is reflected in several small peaks in the lower, medium, and upper parts of the selectivity distribution, an overall characterization hardly seems appropriate. These more heterogeneous educational profiles typical for economic migrants from Eastern Europe are not in line with the theoretical expectations, according to which, compared to refugees, labour migrants should be more favourably selected on education. In contrast, the other empirical selectivity distributions on sex and age largely follow the considerations developed earlier. To investigate how destination choices reflect the purported differences in individuals’ evaluations of the costs, benefits and probabilities associated with different destinations, we contrasted Syrian refugees who migrated to Germany with Syrian refugees who opted for one of their neighbouring states. We argued that, even in dire situations, individuals may not just respond to push conditions but retain agency and – given the necessary resources – might select a destination farther away. Thus, also for refugees who are pushed out in times of war, pull factors come into play. This line of reasoning is based on the premise that migrants who overcome obstacles (e.g., geographic and/or cultural distance) are differentially selected. Our findings for Syrian refugees support this idea. Syrians who settle in Germany are younger, more often male and relatively better educated than Syrians migrating to Jordan or Lebanon. 78 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Our theoretical reasoning focused on a comparison with the population in the origin country. Empirically, we complemented the picture by contrasting migrants to the population in the destination country. The findings on age and educational selectivity illustrate that assessments of selectivity differ considerably depending on which reference population is chosen. While it is correct to characterize refugee migrants as disproportionately young relative to the destination country population, a divergent picture emerges relative to the origin population. In this case, they appear older. For labour migrants, in contrast, it makes little difference whether they are compared to their peers at home or to the destination population: they are composed mainly of younger individuals. Adopting a destination-relative perspective with regard to educational selectivity, we find that refugee migrants are less educated in absolute, as well as relative terms, while relative to the population in the origin country, they tend to be positively selected. Again, for labour migrants, the question of whether they are compared to the origin or the destination country is chosen makes less of a difference. Discrepancies between origin-relative and destination-relative selectivity patterns seem to be related to differences in the composition of the population in the origin and the destination country. Contemporary refugees mostly stem from less developed societies that are characterized by an expansive population structure with many young people at the bottom, and by educational levels well below those typical for modern societies. Recent labour migrants to Germany, in contrast, mostly originate from countries, in which the age structure is more similar to that of the German population and in which educational attainments are indicative of a far advanced educational expansion. Apart from characterizations of recent refugees based on the IAB-BAMF-GSOEP Survey of Refugees, little is known about their composition across European destinations. One exception is a study on Syrian and Iraqi refugees in Austria (Buber-Ennser et al. 2016), which reports very similar findings: migrants from both groups are on average younger and positively selected with respect to education compared to those left behind. However, the account is limited by its relatively small sample size and the use of a selectivity measure at the group level. Overall, we would argue that refugees do not stand out as a special case in need of a special account on selectivity. Our findings instead point to the necessity to move away from broad categorizations of different groups based on migration motives, but instead specify the conditions that are typical for distinct migration streams and for different groups of migrants and then consider these conditions within general models of migratory behaviour. An unresolved question arising from this study is whether origin-relative selectivity indeed captures less tangible resources and latent, often unmeasured characteristics including cognitive and other skills, as well as motivational traits. It is also difficult to tell whether the positive selection on education which we observed for recent refugees in Germany relative to those left behind, can, at least to some extent, com- Selectivity profiles of recently arrived refugees and labour migrants in Germany 79 pensate for their very low levels of absolute education. 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Selectivity profiles of recently arrived refugees and labour migrants in Germany 83 A pp en di x Ta bl e 2: D is tr ib ut io ns fo r r ef ug ee s Af gh an is ta n Er itr ea Ira q Sy ria D E n N % n N % n N % n N % Se x M al e 29 4 36 0 76 13 7 16 4 80 33 6 39 5 74 13 09 15 28 74 Fe m al e 18 0 11 4 24 64 41 20 19 8 13 9 26 75 6 53 7 26 Ab so lu te e du ca tio n IS CE D 0 13 6 95 20 15 8 4 11 5 85 16 13 0 10 3 5 IS CE D 1 15 8 17 1 36 92 98 48 16 7 17 1 32 52 3 43 4 21 IS CE D 2 65 71 15 55 55 27 10 8 11 2 21 44 1 43 4 21 IS CE D 3 63 81 17 27 27 13 61 64 12 43 2 49 6 24 IS CE D 4 9 19 4 0 0 0 9 5 1 49 62 3 IS CE D 5 ,6 43 38 8 12 16 8 74 96 18 49 0 53 7 26 M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n Ag e 32 .6 4 31 27 .1 2 26 33 .4 2 32 34 .5 1 34 Re la tiv e ag e O rig in -r el at iv e 0. 46 0. 46 0. 42 0. 46 0. 50 0. 43 0. 48 0. 41 D es tin at io nre la tiv e 0. 25 0. 22 0. 21 0. 22 0. 30 0. 22 0. 31 0. 22 Re la tiv e ed uc at io n O rig in -r el at iv e 0. 67 0. 68 0. 55 0. 55 0. 50 0. 48 0. 61 0. 64 D es tin at io nre la tiv e 0. 17 0. 02 0. 14 0. 03 0. 20 0. 03 0. 34 0. 14 N ot e: T he IA B- BA M F- G SO EP S ur ve y of R ef ug ee s in G er m an y da ta a re w ei gh te d du e to a n ov er sa m pl in g of fe m al es a nd o ld er im m ig ra nt s. W hi le n re po rt s th e nu m be r of u nw ei gh te d ca se s, N a nd t he p er ce nt ag es (% ) r ef er t o w ei gh te d da ta . T he v al ue s of N a re d er iv ed b y m ul tip ly in g th e to ta l n um be r of im m ig ra nt s in e ac h or ig in g ro up w ith t he w ei gh te d pe rc en ta ge s fo r s ex a nd a bs ol ut e ed uc at io n. F or S yr ia ns in Jo rd an a nd L eb an on t he t ab le p re se nt s un w ei gh te d da ta . 84 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Ta bl e 2: co nt in ue d Sy ria JO Sy ria LB N % N % Se x M al e 11 8 47 11 9 48 Fe m al e 13 5 53 12 8 52 Ab so lu te e du ca tio n IS CE D 0 34 13 31 13 IS CE D 1 74 29 11 0 45 IS CE D 2 88 35 77 31 IS CE D 3 36 14 20 8 IS CE D 4 0 0 0 0 IS CE D 5 ,6 21 8 9 4 M ea n M ed ia n M ea n M ed ia n Ag e 35 .9 1 35 33 .9 4 31 Re la tiv e ag e O rig in -r el at iv e 0. 59 0. 64 0. 55 0. 55 D es tin at io nre la tiv e 0. 58 0. 64 0. 50 0. 50 Re la tiv e ed uc at io n O rig in -r el at iv e 0. 51 0. 54 0. 42 0. 45 D es tin at io nre la tiv e 0. 23 0. 11 0. 15 0. 06 Selectivity profiles of recently arrived refugees and labour migrants in Germany 85 Ta bl e 3: D is tr ib ut io ns fo r l ab ou r m ig ra nt s Au st ria Bu lg ar ia Ch in a Cr oa tia Fr an ce G re ec e N % N % N % N % N % N % Se x M al e 36 7 50 43 0 49 46 7 45 15 3 50 40 4 53 28 0 50 Fe m al e 37 1 50 44 8 51 56 1 55 15 1 50 36 4 47 28 1 50 Ab so lu te e du ca tio n IS CE D 0 0 0 15 1 17 25 2 10 3 14 2 60 11 IS CE D 1 3 0 29 3 5 0 3 1 3 0 17 3 IS CE D 2 37 5 21 3 24 69 7 54 18 33 4 14 0 25 IS CE D 3 28 4 38 27 8 32 47 2 46 15 8 52 18 7 24 13 3 24 IS CE D 4 38 6 40 5 13 1 18 6 0 0 34 6 IS CE D 5 ,6 37 6 51 16 7 19 44 4 43 61 20 53 1 69 17 7 32 M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n Ag e 45 .8 5 47 35 .4 9 34 32 .4 9 31 48 .0 5 50 42 .3 8 43 45 .9 9 48 Re la tiv e ag e O rig in -r el at iv e 0. 31 0. 23 0. 32 0. 32 0. 26 0. 17 0. 34 0. 24 0. 31 0. 24 0. 37 0. 33 D es tin at io nre la tiv e 0. 29 0. 22 0. 31 0. 31 0. 20 0. 13 0. 33 0. 22 0. 29 0. 22 0. 34 0. 31 Re la tiv e ed uc at io n O rig in -r el at iv e 0. 70 0. 87 0. 43 0. 46 0. 74 0. 73 0. 49 0. 48 0. 68 0. 79 0. 49 0. 47 D es tin at io nre la tiv e 0. 68 0. 83 0. 39 0. 34 0. 66 0. 58 0. 45 0. 36 0. 75 0. 84 0. 46 0. 38 86 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Ta bl e 3: co nt in ue d H un ga ry Ita ly Ka za kh st an M or oc co N et he rla nd s Po la nd N % N % N % N % N % N % Se x M al e 44 8 54 51 0 59 11 3 44 20 7 50 33 8 56 20 36 46 Fe m al e 38 4 46 35 7 41 14 4 56 20 4 50 26 2 44 24 18 54 Ab so lu te e du ca tio n IS CE D 0 14 2 0 0 25 10 87 21 14 2 19 9 4 IS CE D 1 14 2 17 2 1 0 20 5 4 1 48 1 IS CE D 2 13 9 17 21 8 25 76 30 11 5 28 99 17 83 2 19 IS CE D 3 31 8 38 22 6 26 10 1 39 14 7 36 18 9 32 19 66 44 IS CE D 4 91 11 35 4 8 3 0 0 46 8 39 2 9 IS CE D 5 ,6 25 6 31 37 1 43 46 18 42 10 24 8 41 10 17 23 M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n Ag e 40 .6 9 39 46 .8 0 48 38 .5 7 38 37 .6 3 36 45 .7 7 46 40 .1 4 39 Re la tiv e ag e O rig in -r el at iv e 0. 36 0. 33 0. 32 0. 29 0. 41 0. 35 0. 33 0. 33 0. 46 0. 40 0. 37 0. 37 D es tin at io nre la tiv e 0. 33 0. 31 0. 32 0. 31 0. 30 0. 22 0. 21 0. 22 0. 37 0. 37 0. 33 0. 31 Re la tiv e ed uc at io n O rig in -r el at iv e 0. 54 0. 47 0. 65 0. 76 0. 33 0. 28 0. 73 0. 86 0. 58 0. 61 0. 47 0. 43 D es tin at io nre la tiv e 0. 52 0. 54 0. 55 0. 60 0. 40 0. 34 0. 31 0. 14 0. 55 0. 61 0. 47 0. 39 Selectivity profiles of recently arrived refugees and labour migrants in Germany 87 Ta bl e 3: co nt in ue d Po rt ug al Ro m an ia Ru ss ia Sp ai n Th ai la nd Tu rk ey N % N % N % N % N % N % Se x M al e 14 8 54 70 4 46 56 4 32 29 0 54 31 14 97 5 49 Fe m al e 12 4 46 83 7 54 11 83 68 24 8 46 18 9 86 10 12 51 Ab so lu te e du ca tio n IS CE D 0 48 18 11 3 7 81 5 11 2 29 13 43 0 22 IS CE D 1 18 7 23 1 14 1 7 1 16 7 12 5 6 IS CE D 2 57 21 40 6 26 33 2 19 57 11 83 38 69 1 35 IS CE D 3 58 21 52 5 34 50 8 29 15 2 28 45 20 42 7 21 IS CE D 4 10 4 10 5 7 55 3 15 3 5 3 57 3 IS CE D 5 ,6 81 30 36 9 24 75 7 43 29 6 55 41 19 25 7 13 M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n Ag e 42 .9 3 44 38 .3 5 37 39 .5 7 39 43 .9 6 45 40 .4 8 41 42 .9 7 43 Re la tiv e ag e O rig in -r el at iv e 0. 32 0. 31 0. 33 0. 25 0. 35 0. 28 0. 28 0. 21 0. 38 0. 36 0. 30 0. 31 D es tin at io nre la tiv e 0. 31 0. 31 0. 29 0. 22 0. 32 0. 22 0. 27 0. 22 0. 32 0. 31 0. 22 0. 22 Re la tiv e ed uc at io n O rig in -r el at iv e 0. 51 0. 58 0. 49 0. 50 0. 46 0. 29 0. 65 0. 76 0. 51 0. 54 0. 41 0. 39 D es tin at io nre la tiv e 0. 41 0. 33 0. 44 0. 36 0. 56 0. 58 0. 66 0. 83 0. 30 0. 12 0. 29 0. 12 88 Christoph Spörlein/Cornelia Kristen/Regine Schmidt/Jörg Welker Ta bl e 3: co nt in ue d U kr ai ne U ni te d Ki ng do m U SA N % N % N % Se x M al e 13 5 26 36 5 71 34 9 56 Fe m al e 37 8 74 15 1 29 27 6 44 Ab so lu te e du ca tio n IS CE D 0 16 3 7 1 10 2 IS CE D 1 2 0 0 0 0 0 IS CE D 2 73 14 43 8 37 6 IS CE D 3 16 6 32 11 6 22 17 0 27 IS CE D 4 25 5 18 3 0 0 IS CE D 5 ,6 23 1 45 33 2 64 40 8 65 M ea n M ed ia n M ea n M ed ia n M ea n M ed ia n Ag e 39 .5 6 38 46 .1 8 47 40 .7 6 42 Re la tiv e ag e O rig in -r el at iv e 0. 37 0. 35 0. 40 0. 35 0. 39 0. 35 D es tin at io nre la tiv e 0. 33 0. 31 0. 37 0. 31 0. 35 0. 31 Re la tiv e ed uc at io n O rig in -r el at iv e 0. 49 0. 45 0. 66 0. 76 0. 69 0. 81 D es tin at io nre la tiv e 0. 60 0. 65 0. 70 0. 83 0. 71 0. 83 Selectivity profiles of recently arrived refugees and labour migrants in Germany 89

Abstract

Migrant selectivity refers to the idea that immigrants differ in certain characteristics from individuals who stay behind in their country of origin. In this article, we describe the selectivity profiles of recent migrants to Germany with respect to educational attainment, age and sex. We illustrate how refugees differ from labour migrants, and we compare the profiles of Syrian refugees who successfully completed the long journey to Europe to Syrian refugees who settled in neighbouring Lebanon or Jordan. We rely on destination-country data from the IAB-BAMF-GSOEP Survey of Refugees, the Arab Barometer, and the German Microcensus, as well as on a broad range of origin-country data sources. Regarding sex selectivity, males dominate among refugees in Germany, while among economic migrants, sex distributions are more balanced. Relative to their societies of origin, labour migrants are younger than refugees. At the same time, both types of migrants are drawn from the younger segments of their origin populations. In terms of educational attainment, many refugees compare rather poorly with average Germans’ attainment, but well when compared to their origin populations. The educational profiles for labour migrants are mixed. Finally, Syrians who settle in Germany are younger, more likely to be male and relatively better educated than Syrians migrating to Jordan or Lebanon.

Zusammenfassung

Migranten unterscheiden sich in bestimmten Merkmalen von Personen, die im Herkunftsland verbleiben. Der vorliegende Beitrag widmet sich der Beschreibung dieser Selektivitätsprofile. Das Augenmerk richtet sich auf die Charakteristiken Bildung, Alter und Geschlecht. Einerseits wird untersucht, welche Unterschiede zwischen Geflüchteten und Arbeitsmigranten zu beobachten sind; andererseits werden syrische Geflüchtete, welche den weiten Weg nach Europa auf sich genommen haben, mit syrischen Geflüchteten verglichen, die in die benachbarten Länder Libanon oder Jordanien gewandert sind. Die Analysen stützen sich auf zwei Datensätze zu Neuzuwanderern in Deutschland, die IAB-BAMF-SOEP Befragung von Geflüchteten und den Mikrozensus, sowie auf den Arab Barometer, der Informationen zu Syrern in Jordanien und dem Libanon beinhaltet. Außerdem wird eine Vielzahl von Datenquellen aus den jeweiligen Herkunftsländern genutzt. Während unter Geflüchteten in Deutschland vor allem Männer vertreten sind, erweist sich die Geschlechterverteilung unter Arbeitsmigranten als ausgeglichener. Darüber hinaus sind Arbeitsmigranten, verglichen mit der Bevölkerung im jeweiligen Herkunftsland, jünger als Geflüchtete. Gleichzeitig zeigt sich für Arbeitsmigranten und Geflüchtete gleichermaßen, dass sie zu den jüngeren Segmenten ihrer jeweiligen Herkunftsgesellschaft gehören. Hinsichtlich der Bildungsselektivität lässt sich festhalten, dass Geflüchtete im Vergleich zur deutschen Bevölkerung zwar zumeist schlechter gebildet sind, dass sie jedoch im Vergleich zur Herkunftsbevölkerung einen höheren Bildungsstand aufweisen. Die Bildungsprofile von Arbeitsmigranten erweisen sich dagegen als heterogen. Syrische Geflüchtete, die sich in Deutschland niedergelassen haben, sind jünger, häufiger männlich und vergleichsweise besser gebildet als Syrer, die nach Jordanien oder in den Libanon gewandert sind.

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Abstract

Soziale Welt is one of the important journals within German sociology and is even read in foreign countries. It includes empirical and theoretical contributions from all areas of the subject and tries to portray the development of sociology and to give a new impetus. In addition to the quarterly published issues, there are special issues with a unified theme.

The journal "Soziale Welt" is aimed at sociologists, social scientists, and at generally interested readers.

Website: www.soziale-welt.de

Zusammenfassung

Die Soziale Welt ist eine der großen, auch im Ausland gelesenen Fachzeitschriften innerhalb der deutschen Soziologie. Sie bringt empirische und theoretische Arbeiten aus allen Bereichen des Faches und versucht auf diese Weise, die Entwicklung der Soziologie einerseits zu spiegeln und ihr andererseits auch neue Impulse zu geben. Dies geschieht neben den viermal pro Jahr erscheinenden regulären Heften auch durch die Arbeit an Sonderbänden mit einheitlicher Thematik.

Die Zeitschrift "Soziale Welt" wendet sich an Soziologen, Sozialwissenschaftler, Interessierte allgemein.

Homepage: www.soziale-welt.de