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Employment Trajectories of Recent Immigrants in Switzerland

  • Elena Vidal-CosoEmail author
Open Access
Chapter
Part of the IMISCOE Research Series book series (IMIS)

Abstract

This chapter analyses immigrants’ labour market trajectory throughout their settlement in Switzerland considering their employment situation in the country of origin and the characteristics of the family migration process. The investigation is particularly interested in analysing whether the observed inequalities by origin in the labour market integration result from differences in terms of human capital among immigrants. However, labour disadvantages existing and persisting over time for some immigrant groups, regardless of their human capital characteristics and their level of assimilation in Switzerland, would confirm segmentation and discrimination postulates. Analysis revealed lower employment probabilities for some groups of immigrants immediately after moving to Switzerland once human capital differentials are controlled for. However, results are also consistent with the assimilation hypothesis because the analysis points to an overall improvement in immigrants’ probability of being employed during the process of settlement in Switzerland. Nevertheless, significant differences have emerged by gender. In comparison with men, women are more likely to be unemployed, to be inactive or to work part-time. Finally, results corroborate that post-migration employment is lower for tied migrants and family-motivated migrants. Moreover, family-motivated migration has only temporary effects on labour insertion of male migrants, whereas it harms employment prospects for women more permanently.

Keywords

Labour market trajectories Settlement Human capital Gender Reasons for migration Switzerland 

6.1 Introduction

The employment and socioeconomic integration of immigrants in destination countries has attracted considerable public and policy concern. Understanding how successfully immigrants from different origins integrate in host labour markets and the specificities of employment and occupational attainment of female and male migrants are relevant issues for further policy implementations. Indeed, academic interest has broadly focussed on immigrants’ employment status and the types of job they hold in comparison with native workers. Likewise, scientific investigation has attempted to discern the effect of residence length in the convergence of immigrants’ labour market performance compared with that of the native-born (Chiswick 1978; Chiswick et al. 2005; Akresh 2006, 2008). However, although a highly relevant issue, studies on labour mobility of immigrants between their home country and host country are scarce due to the longitudinal data requirements for such analysis, which is only available for some countries (some interesting studies are Akresh 2006, 2008 for the United States of America; Chiswick et al. 2005 for Australia; Bauer and Zimmerman 1999 for Germany; Rooth and Ekberg 2006 for Sweden; and Stanek and Veira 2009; Aysa-Lastra and Cachón 2013; Simón et al. 2014; Vidal-Coso and Miret-Gamundi 2014 for Spain.)

Employing the longitudinal information from the new data on migration in Switzerland, the Migration-Mobility Survey, this chapter focusses on immigrants’ trajectories with respect to their employment status at the time of migration and during the process of settling in Switzerland. Moreover, the analysis discerns how the integration into the host labour market varies across origins and gender. This survey is the first in the country containing retrospective information on immigrants’ employment status before migration together with their first and their current employment status in Switzerland. The richness of the information contained in the survey allows searching for the main explanatory factors with respect to immigrants’ participation in the labour market at different points in their migratory experience.

The investigation is particularly interested in analysing whether the observed inequalities by origin in the labour market integration of immigrants after arrival and during settlement in Switzerland result from differences in terms of human capital across origins. However, if in contrast labour market disadvantages exist and persist over time for those migrants more distant linguistically and culturally, regardless of their human capital characteristics and previous experience in the labour market, this situation would confirm the segmentation and discrimination postulates. This chapter also aims to investigate whether there are differences between the genders in immigrants’ labour market attainment. In particular, the availability of information in relation to the migration process allows us to estimate whether post-migration labour performance of women is more determined by family characteristics (partnership, couple’s migratory trajectory and presence of children) and reasons for migration (i.e., professional or family-oriented migration) than that of men because female-tied migration is associated with lower labour attachment due to the persistence of the preference for men’s careers (Boyle et al. 2001, 2009).

Switzerland constitutes the perfect case study for achieving our research interests because of its unique structural and institutional characteristics. On the one hand, Swiss migration policy has reoriented towards a selective model favoring the highly skilled citizens in an increasingly globalized scenario. Some authors (Afonso 2004; Favre 2011; Wanner 2004) identified, at the beginning of the 2000s, a shift in the occupational profile of the foreign labour force in Switzerland. They pointed out the overrepresentation at the top of the occupational structure (Favre 2011) of the recently arrived highly skilled immigrants, in speciall of those from the European Union (EU) and North America (Golder 2001; Laganà 2013; Liebig et al. 2012; Wanner et al. 2002; Widmer 2005). Nevertheless, they also maintain that the entrance of low-skilled immigrants has not concluded and that migration is dual in terms of human capital, with a concentration of new immigrants at the bottom of the occupational structure. Empirical evidence pointing to disadvantages concerning the labour performance of specific groups of immigrants in Switzerland is abundant, linked not only to their lower educational level but also to discriminatory practices and to the segmented nature of the Swiss labour market (Ebner and Helbling 2016; Fibbi et al. 2003; Widmer 2005; Vidal-Coso and Ortega-Rivera 2017). On the other hand, some of these studies also found gender-specific differences in labour and occupational outcomes among immigrants, with women presenting less favourable labour attainment than men did. Whereas the dynamism of the Swiss labour market would act to facilitate the labour insertion of men and women partners, public attitudes towards employment remain highly gendered in the country (Levy and Widmer 2013; Stadelmann-Steffen 2007). Among OECD countries, Switzerland has one of the highest participation rates of its female population but at the same time one of the lowest female full-time employment rates (Dutu 2014). The scarce and expensive provision of external childcare encourages mothers either to withdraw from employment or to work part-time (Stadelmann-Steffen 2007). Deficiencies in conciliatory measures reinforce inequalities across socio-economic groups, which can be even more crucial for immigrant families in the absence of a family network. Moreover, previous analysis demonstrates the importance of different job opportunity structures in explaining the heterogeneity of how women in Switzerland adapt their labour supply to their family circumstances depending upon their national origin (Vidal-Coso 2018). To summarize, due to the country’s economic prosperity, job opportunities and wage standards, immigrants are expected to successfully integrate into the Swiss labour market. Thus, this study provides the first opportunity to explain the degree of success of the integration of immigrants into the Swiss labour market considering their migration motivations, their family circumstances during their mobility into Switzerland, and their human capital and labour situation in origin and destination.

The chapter tries to enlarge the empirical evidence of the Migration-Mobility Nexus. Although the research is based in the paradigm of immigrants’ integration into the host society through patterns of labour market participation, it aims to emphasize the notion of migration as a mobility process and of the individuals’ trajectories within the context of an international division of labour and human capital in a contemporary globalized migratory scenario. Indeed, Switzerland constitutes a perfect case for analysing this Migration-Mobility Nexus because the country is characterized by a dual regime of migration. The chapter aims to understand to what extent this dual regime affects mechanisms of inclusion and exclusion of migrants in labour markets.

This chapter is structured as follows. Section 6.2 reviews the theoretical framework of the labour mobility of migrants. The data, the variables and the sample used are described in Sect. 6.3. Sections 6.4 and 6.5 present the research results. Finally, the chapter concludes with a discussion Sect. (6.6).

6.2 Theoretical Background and Research Objectives

The labour market mobility of immigrants has been widely analysed from different theoretical perspectives. First, the functionalism and neoclassical traditions focus on the importance of human capital and time of residence as key determinants of the differences of individuals in labour market attainment (Borjas 1994; Chiswick 1978). This perspective maintains that, upon their arrival in the host country, immigrants suffer a disadvantage relative to natives that can affect different aspects of their labour market incorporation, such as employment, wages and devaluation in the occupational category. This disadvantage has been attributed to the difficulties of immigrants in transferring formal education, employment experience, and training obtained abroad (Chiswick and Miller 2009; Clark and Drinkwater 2008). Moreover, some authors have highlighted the lack of country-specific skills on arrival as a key factor explaining differences in economic success. Limited knowledge about the functioning of the labour market or a lack of fluency in the host country’s language might represent an obstacle to finding better job opportunities for immigrants (Chiswick and Miller 2002; Dustman and Fabri 2003; Clark and Drinkwater 2008). A key factor in determining new immigrants’ labour performance is the extent to which their education, pre-migration labour market experience, and training obtained abroad are valued in the destination country (Blackaby et al. 2002; Clark and Drinkwater 2008; Kanas and Van Tubergen 2009). Moreover, the level of transferability of human capital across countries depends upon the economic and cultural proximity between country of origin and country of destination (Akresh 2006). From this perspective, the assimilation process entails that the initial disadvantage should decline over time. As immigrants settle into the receiving country, they adapt their skills to the requirements of the destination labour market, improve their knowledge of the host country’s language, and acquire local education and training. These adjustments can eventually improve their employment prospects. Consequently, a U-shaped pattern of employment and occupational mobility for immigrants is expected, with an occupational downgrading and higher risk of unemployment upon arrival and a recovery in employment probabilities and conditions during settlement in the host country (Chiswick et al. 2005).

The second theoretical framework considered in this investigation is the structural or dual labour market theory (Piore 1979; Thurow 1975), which offers an alternative explanation of immigrants’ employment after migration. According to this view, labour markets are divided into primary and secondary sectors. The primary sector offers stable jobs, relatively high wages, and better working conditions. In contrast, the secondary sector is characterized by unstable jobs with low salaries and worse job conditions. This perspective predicts greater employment instability and precariousness and consequently higher risk of unemployment in the secondary segment and little intersegment mobility, particularly upward mobility, from secondary to primary segment occupations. Furthermore, Piore (1979) and Kalleberg and Sorensen (1979) identified the segmentation of the labour force by migrant status or national origin of workers. Various researchers, such as Heath and Cheung (2007), refer to an “ethnic penalty” or the process in which immigrants from a particular national or ethnic origin experience some type of disadvantage to their labour performance. This situation involves a greater risk of unemployment and of a limitation on their labour prospects to access the secondary segment of the labour market compared with natives with similar human capital and socio-demographic profiles (Rooth and Ekberg 2006 for the Swedish context). This approach maintains that employment disadvantage and occupational downgrading of some groups of migrants is expected to persist over time, regardless of the duration of the residence and the adaptation of skills to the host country’s labour market requirements. In a very similar line of argumentation, such researchers state that cultural or social differences between individuals from different cultures can cause employers to prefer applicants from their own culture or with higher affinity (Ebner and Helbling 2016). To cope with this problem, employers rely on the observable characteristics of applicants, such as gender, age, national origin and ethnic group, to infer their expected productivity, which should correlate with the perceived average productivity of the group. A case study for the Swiss case can be found in Fibbi et al. (2006).

The third theoretical perspective considered is social capital theory (Palloni et al. 2001; Massey et al. 1993). The starting hypothesis is that once someone in a person’s networks migrates, the ties of friendship and kinship are transformed into a resource for gaining access to employment at the destination, particularly in those considered “immigrant jobs” (Massey et al. 1994) because social and family networks make a migrant’s economic requirements less urgent, facilitating an optimization in the job search. However, Portes and Sensenbrenner (1993) noted that social networks might have negative consequences for migrants and that the same mechanism that gives rise to labour insertion at a destination can also constrain occupational opportunities and labour mobility to certain types of jobs. In this sense, Mahuteau and Junankar (2008) confirm the negative effect of social networks leading to poorer occupational labour outcomes.

Finally, the labour performance of migrants has largely been assumed to vary with individuals’ family circumstances and reasons to migrate. Accordingly, migrants who migrate to join their relatives at the destination are commonly expected to decline in their occupational attainment in comparison to employment-motivated migrants. Mincer (1978) analysing “Family Migration Decisions”, stated that the migration of “tied” partners was motivated by a desire to maximize family income rather than by individual professional interests. This motivation would place them in a less favourable position in the host labour market. The author determined that tied movers were primarily women and that those women were more likely to be unemployed or out of the labour force and have lower earnings than were otherwise comparable immigrant women who were primary movers. As a matter of fact, it was usually the husband who stimulated employment-motivated family migration and who had the most to gain from the family relocation (Boyle et al. 1999). Currently, the massive entrance of young women into the domains of higher education and professional careers, together with the reversal in the gender gap in education (Esteve et al. 2016), explains the emergence of dual-income couples, in particular among people with a university degree. Accordingly, the “trailing spouse” resulting from migration can no longer be assumed, particularly among highly skilled migrants. However, although differences between men and women in terms of labour market outcomes after migration have decreased over time, they have not completely disappeared. In contrast with the commonly held view that highly skilled couples are very egalitarian, arrangements consolidating gender inequality following family migration might be present among skilled in addition to among unskilled migrants. Indeed, the persistence of the preference for men’s careers (Boyle et al. 2001) in family migration decision making makes it necessary to estimate whether the post-migration labour performance of women is more determined by the reasons for migration than is that of men.

This chapter analyses immigrants’ labour market trajectories in Switzerland considering their employment situation in the country of origin and throughout their settlement in the country of destination. The research has three specific objectives. First, it provides new insight to understand the dynamics of immigrants’ insertion in the host labour market, focussing on their employment status at the time of migration and during the process of settlement in Switzerland. In doing so, the analysis aims to search for differences by birthplace and gender in the individual’s probability of being employed in relation to being unemployed or inactive. Furthermore, for those employed at the time of the survey, the analysis estimates their probability of working part-time.

Second, this research is particularly interested in discerning whether the observed differences in terms of employment status result from the differences among the considered groups of immigrants in terms of skill composition and of the degree of assimilation of their human capital to the requirements of the Swiss labour market. However, labour disadvantages existing and persisting over time for some immigrant groups, regardless of their human capital characteristics and level of assimilation to Switzerland, would confirm segmentation and discrimination postulates.

Third, professional and family reasons for migration are not necessarily incompatible. Thus, this research will assess to what extent the probability of unemployment, economic inactivity and part-time employment is higher for tied or family-motivated migrants. Or if on the contrary, family-motivated mobility following the primary migrant partner helps the migrant to successfully integrate into the host labour market, due to less urgent economic requirements and a greater ability to be selective about the types of jobs he/she takes. Moreover, the research aims to demonstrate whether gender dispositions are essential for explaining family migration labour market outcomes. If they are essential, then women’s labour market participation would be more determined by their family circumstances and the reason for migration than would be that of men. This gender perspective is indispensable for analysing the Swiss case. In effect, whereas the dynamism of the Swiss labour market should facilitate the labour insertion of both partners, the gendered patterns of the female labour supply might generate or reinforce gendered employment arrangements among migrant couples.

6.3 Data and Methods

The data source used in the empirical analysis is the Swiss Migration-Mobility Survey. From the original sample, this research focusses on the following nine groups: Germany/Austria, France, Italy/Spain, Portugal, UK/North America, India, South America, Africa and Asia. This grouping yields a final sample selection of 5823 immigrants – 3118 immigrant men and 2705 immigrant women. The data is weighted for descriptive and multivariate analysis. This data source is the best option for the analytical purposes of this investigation because it includes retrospective information about employment trajectories from the period prior to migration. It includes information on the immigrant’s employment status at three crucial points: before migration to Switzerland, immediately upon arrival, and at the time of data collection. This information allows analysis of the dynamics in terms of employment status at the time of migration and during the process of settling in Switzerland. Nonetheless, the Migration-Mobility Survey is composed of single cross-sectional data. As Chiswick et al. (2005) noted, by using cross-sectional data, we could incur a bias in the longitudinal effect that immigrants experience in their labour market achievements. This bias could be a consequence of changes over time in the quality of immigrant cohorts (Borjas 1985), selectivity in return migration, third-country migration or abandonment of the labour market (Constant and Massey 2003) or changing economic cycles (Aslund and Rooth 2007). However, in this research, migrants only arriving recently, between 2006 and 2016, are considered. More specifically, immigrants considered in this analysis have a median duration of residence of 5 years in Switzerland. Consequently, the recent nature of the immigration included in the analysis minimizes the effect of any possible bias.

Using a multinomial probit model, we analyse differentials by birthplace and gender in terms of labour market inclusion, both immediately after their arrival in Switzerland and at the time of data collection. Multinomial probit regression is an extension of probit regression that is applied to categorical variables with more than two categories. We estimate the likelihood of immigrants to be unemployed and to be inactive relative to being employed (which is the baseline category). Thus, the multinomial model is appropriate here, given the unranked nature of the outcome. Moreover, binary probit regression analysis is applied to investigate the immigrant’s probability of working part-time for those individuals employed at the time of the interview. Beta coefficients, the standard errors and the level of signification are specified in the models. Predictive margins from the binary and multinomial models are displayed for interactions between gender and nationality and between gender and reasons for migration.

With respect to explicative human capital variables, we include the level of education (primary or lower, secondary and tertiary) and previous occupation status in the origin country. To control the potential effects of language skills on labour market performance, we include a variable that identifies whether the main language of the individual corresponds to any of the Swiss languages, English or to another foreign language. When models focus on the immigrants’ employment status at the time of the survey, whether the educational level was validated in Switzerland and whether the individual is proficient in the local language are also considered.

Reason for migration with its interaction with gender is included in the models. This variable is coded in four categories: professionally motivated migration, family-motivated migration, professionally and family-motivated migration (including immigrants who declared both reasons of migration, professional and family) and migration motivated by other reasons (e.g., lifestyle). Likewise, age (continuous and squared) and retrospective information concerning family characteristics (children and partnership) are also considered in all models. Social network at arrival is included as a covariate in the analysis of employment status at arrival. When the analyses relate to the moment of data collection, i.e., current employment status, controls for years residing in the country (continuous) are included.

6.4 Descriptive Findings

Table 6.1 displays the employment status of immigrant men and women by country of birth in three different moments of their migratory experience: before migration, immediately after their arrival in Switzerland, and at the time of the survey. According to the observed trajectories in terms of labour market situation, recent male migration successfully assimilates into the Swiss labour market after settlement in the host country. Indeed, although men present, after arrival in Switzerland, lower levels of full-time employment and higher percentages of unemployment in comparison to their labour situation prior to migration, this less favourable labour performance is a temporary process of adjustment to the host country. Effectively, in comparison with their situation in their countries of origin, the majority of male groups present, at the time of the survey and after having resided in the host country, higher employment percentages, particularly of full-time employment, and lower unemployment percentages. Consequently, accordingly to the assimilation postulates, settlement in Switzerland results in a general gain in the employment prospects for immigrant men.
Table 6.1

Employment status of immigrants in country of origin, immediately after migration to Switzerland and at the time of the interview by country of birth and gender (in %)

 

Germany/Austria

France

Italy/Spain

Portugal

UK/North America

India

South America

Africa

Asia

Men

Full-time employment

In origin

72.6

75.3

70.5

71.9

82.8

88.8

69.5

62.6

77.7

After arrival

77.0

84.3

69.4

71.5

76.2

84.7

56.6

46.7

77.1

At the time of interview

84.0

86.7

81.8

80.3

79.0

89.0

66.6

64.0

86.3

Part-time employment

In origin

6.9

3.9

9.8

5.9

6.2

3.2

13.6

13.2

1.9

After arrival

4.5

5.0

9.7

11.9

6.0

3.9

15.1

15.6

3.1

At the time of interview

8.0

6.1

10.3

10.1

6.7

4.3

17.3

14.9

3.4

Seeking a job

In origin

9.5

8.2

8.8

13.8

2.9

0.2

7.3

15.5

4.8

After arrival

10.2

5.5

13.4

12.2

6.3

6.6

13.6

23.8

12.5

At the time of interview

3.9

4.2

4.4

7.4

5.8

4.7

8.3

15.0

5.2

Training/Studies

In origin

9.3

10.2

7.6

4.2

4.0

6.2

4.8

3.0

3.4

After arrival

5.7

2.4

3.9

1.4

3.3

3.5

4.2

6.2

2.2

At the time of interview

2.2

1.2

1.2

1.7

0.9

2.5

1.5

Home/Family

In origin

0.1

0.1

0.4

1.3

0.6

0.8

0.4

0.8

After arrival

1.2

0.7

0.6

3.9

0.5

3.8

2.6

2.2

At the time of interview

0.1

0.6

0.3

0.3

3.5

0.8

1.7

3.0

0.7

Disabled/Retired/Pensioner/Other non-employed

In origin

1.7

2.3

3.2

3.9

2.8

0.9

4.0

5.3

11.3

After arrival

1.4

1.9

3.5

2.4

4.3

0.8

6.8

5.1

2.9

At the time of interview

1.8

1.2

2.0

2.0

3.3

0.3

3.5

1.5

4.5

Observations (N), not weighted. Total = 3118

593

325

548

302

450

338

237

268

57

%

19.0

10.4

17.6

9.7

14.4

10.8

7.6

8.6

1.8

Women

Full-time employment

In origin

58.2

61.4

49.6

60.8

61.7

53.4

55.2

46.8

52.7

 

After arrival

57.3

58.3

41.0

41.9

44.7

20.1

26.6

36.0

49.5

 

At the time of interview

58.6

60.4

47.8

50.3

45.5

27.6

26.5

31.6

60.5

Part-time employment

In origin

15.9

11.0

16.1

13.5

18.7

10.3

18.2

20.4

12.0

 

After arrival

15.6

12.8

12.8

20.3

7.7

5.6

18.1

15.1

8.8

 

At the time of interview

30.2

23.9

26.4

36.4

19.5

8.6

33.8

25.0

10.5

Seeking a job

In origin

5.9

4.7

11.5

14.9

1.6

6.0

5.9

3.7

13.3

 

After arrival

10.9

12.0

16.7

22.1

12.3

19.5

18.3

18.5

17.7

 

At the time of interview

3.9

4.9

9.9

5.1

9.7

18.2

14.2

16.1

5.8

Training/Studies

In origin

15.2

15.1

11.1

3.2

5.0

7.5

7.9

8.0

10.7

 

After arrival

9.2

5.1

9.1

2.1

5.7

5.8

9.3

3.7

1.8

 

At the time of interview

0.5

2.3

2.4

0.0

1.1

3.4

2.2

4.0

Home/Family

In origin

2.2

4.4

8.1

3.7

10.6

20.8

7.9

17.0

10.6

 

After arrival

4.8

8.0

15.2

10.0

25.5

44.2

18.1

20.9

21.5

 

At the time of interview

5.2

5.5

12.5

5.2

20.1

41.0

17.0

19.8

20.5

Disabled/Retired/Pensioner/Other non-employed

In origin

2.6

3.3

3.6

3.8

2.5

2.1

4.8

4.0

0.8

 

After arrival

2.2

3.8

5.1

3.6

4.0

4.8

9.6

5.7

0.8

 

At the time of interview

1.6

3.1

1.0

3.1

4.1

1.2

6.2

3.5

2.2

Observations (N), not weighted. Total = 2705

462

209

373

222

417

250

464

244

64

%

17.1

7.7

13.8

8.2

15.4

9.2

17.2

9.0

2.4

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

However, heterogeneity across origins in post-migration labour market integration could also be observed among immigrant men. On the one hand, men from Germany, Austria, France, Italy, Spain, Portugal, and Asia are the most successful in their employment incorporation in Switzerland. The initial devaluation in employment status is minimal for them, and the gain in terms of employment is very steady for these groups of migrants after residing in Switzerland. On the other hand, lower percentages of full-time employment and higher unemployment among immigrant men from South America and Africa indicated more-disadvantaged labour insertion for them. Finally, other reasons for labour market inactivity are residual among male immigrants. To summarize, although integration into the Swiss labour market has been very positive in general for recent male migration, this statement is not true for those born in Africa and South America. Indeed, their migration to Switzerland does not improve the disadvantaged employment status they presented before migration.

The employment trajectories of immigrant women during their migratory process to Switzerland follow a different pattern; mobility into Switzerland has caused a reduction of their labour market attachment. Indeed, with the exception of women from Germany/Austria and Asia, percentages of full-time employment among immigrant women are markedly reduced after migration in comparison to the situation in origin. Moreover, a notorious rise in the share of part-time employment during their migratory process is observed, particularly among women from Germany/Austria, France, Italy/Spain, Portugal and South America. Unemployment percentages have also increased among female immigrants from UK/North America, India, South America and Africa. Finally, the increase in the post-migration percentages of women who care for the home/family, particularly of non-European women, is also notorious. Furthermore, the migration to Switzerland has increased the existing differences before the mobility process in terms of participation in the labour market between the genders. Effectively, the labour participation of women in their countries of origin was already lower than was that of their male counterparts because women presented lower percentages of full-time employment, higher percentages of part-time employment and a higher share of inactivity for family reasons. These existing differences by gender were accentuated after migration to Switzerland. Whereas employment prospects after settling in Switzerland improved in general for immigrant men and even exceed the employment percentages in the country of origin, the recovery in labour market participation after residing in the country is not so evident for immigrant women, at least in terms of full-time employment.

Table 6.2 displays the employment and occupational characteristics of immigrant men and women at the time of data collection. From this table, it is interesting to highlight the highly skilled occupational profile of male immigrants from the German-speaking countries, France, UK/North America and India. Table 6.2 also shows that all employed immigrants, men or women, and independently of their origin declare that their professional situation has improved in relation to that in the country of origin at the time prior to migration. However, although the feeling of professional progression after moving to Switzerland is systematic among all immigrants, some heterogeneity could be observed across origins. Male migrants from Germany and Austria, France and UK/North America express lower levels of improvement and a higher level of status quo. In fact, these groups are highly educated, many of them with director and management positions. Thus, one possible interpretation of the results is that these migrants were characterized by relatively good conditions prior to the migration and, consequently, the differences between the evaluation of the situation in the home country and in Switzerland are smaller. In contrast, despite their relatively low insertion in terms of employment, occupational category and higher overqualification, immigrants from India, South America and Africa, but also from Italy, Spain, Portugal, and Asia express very high levels of improvement in their professional situation. Gender-specific patterns among employed immigrants also appear in Table 6.2. For example, there are higher proportions of directors and managers among employed men than among employed women, and there are major levels of overqualification among the latter.
Table 6.2

Occupational characteristics of immigrants at the time of interview by origin and gender (in %)

 

Germany/Austria

France

Italy/Spain

Portugal

UK/North America

India

South America

Africa

Asia

Men

Occupation

 Director, managerial

29.9

36.8

27.1

7.6

48.8

34.0

21.4

22.0

34.2

 Self-employed

6.4

6.1

17.4

21.1

4.3

2.8

14.3

6.0

3.6

 High-skilled employee

35.6

34.6

26.6

6.8

34.6

51.8

26.1

15.3

28.3

 Skilled employee

24.9

19.9

19.6

32.2

9.7

7.7

18.0

28.5

18.3

 Unskilled employee

3.3

2.7

9.4

32.3

2.6

3.6

20.1

28.2

15.6

Industry

 Agriculture, forestry and fishing

0.1

1.0

1.1

6.3

2.1

0.0

2.6

0.5

 Industry

17.3

21.1

8.3

8.4

14.3

9.2

9.1

9.6

1.8

 Construction

14.0

9.6

18.8

44.8

2.7

0.9

7.9

12.9

17.2

 Sales, trade, transportation, hotels and restaurants

11.7

11.3

19.3

13.9

6.9

5.0

16.7

20.1

19.2

 Information and communication

12.2

4.7

3.7

1.3

9.3

29.0

3.9

1.6

6.5

 Finances and insurance

8.2

9.0

7.2

1.2

14.2

7.7

7.9

9.2

10.7

 Real State

0.7

1.1

0.7

0.4

0.2

0.0

0.6

0.0

0.9

 Professional, scientific, technic, admin

9.9

17.5

21.6

3.9

22.6

29.3

11.7

14.7

18.2

 Public administration, defence, education, health, social action

14.6

12.5

6.4

3.2

12.5

8.3

10.8

12.9

18.7

 Other

11.4

12.2

12.8

16.6

15.2

10.7

28.8

18.4

6.8

Overqualified

18.9

17.8

25.5

32.6

15.0

14.1

35.8

29.9

34.2

Job improvement

 Improved substantially

48.5

41.1

55.2

47.3

38.2

53.9

49.1

46.6

47.6

 Improved slightly

23.7

30.8

29.5

36.9

33.2

32.6

30.6

38.4

37.9

 Remained the same

20.6

18.3

9.8

9.5

17.7

8.4

9.8

7.0

7.5

 Worsened slightly

5.6

7.4

3.7

4.4

8.3

2.9

3.7

6.7

4.6

 Worsened substantially

1.5

2.5

1.8

2.0

2.6

2.2

6.8

1.3

2.5

Women

Occupation

 Director, managerial

16.4

21.3

8.9

3.5

25.1

17.5

8.5

21.2

12.0

 Self-employed

5.2

4.0

14.0

10.0

9.0

5.7

12.5

4.9

10.3

 High-skilled employee

39.5

48.3

45.9

9.6

53.0

61.5

18.0

12.5

69.4

 Skilled employee

30.0

19.5

13.9

17.9

10.7

12.0

19.3

23.0

3.3

 Unskilled employee

8.9

7.0

17.3

59.0

2.3

3.2

41.7

38.4

4.9

Industry

 Agriculture, forestry and fishing

0.1

0.6

0.5

5.6

0.7

0.0

1.6

0.6

--

 Industry

5.9

13.0

7.6

7.7

4.6

6.3

4.6

10.1

1.9

 Construction

5.8

2.5

4.8

0.4

0.7

0.0

2.2

0.0

13.3

 Sales, trade, transportation, hotels and restaurants

15.1

12.5

13.9

22.5

5.0

4.6

17.3

28.4

16.7

 Information and communication

5.4

4.4

2.7

0.0

4.6

24.0

3.1

1.5

16.7

 Finances and insurance

3.2

8.2

4.5

0.0

11.1

8.6

4.7

1.8

3.7

 Real State

1.0

1.5

0.9

1.3

1.5

 Professional, scientific, technic, admin and support services

10.5

10.5

21.8

6.0

22.2

26.0

13.6

9.4

8.4

 Public administration, defence, education, health, social action

32.6

26.5

13.8

13.5

33.0

18.4

12.6

15.0

7.0

 Other

20.5

20.1

29.5

43.0

16.6

12.1

40.4

33.3

32.2

Overqualified

27.3

25.0

37.6

50.2

24.2

25.7

60.3

39.3

14.2

Job improvement

 Improved substantially

48.8

41.3

55.9

48.1

38.9

54.9

51.6

51.6

48.5

 Improved slightly

23.2

31.8

29.2

35.8

34.6

32.9

29.2

33.0

38.6

 Remained the same

21.0

18.7

10.3

10.0

17.1

7.9

10.7

7.5

7.1

 Worsened slightly

5.5

6.1

3.5

4.0

8.0

2.7

2.6

7.1

3.2

 Worsened substantially

1.4

2.2

1.1

2.2

1.5

1.6

5.9

0.8

2.5

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

6.5 Multivariable Results

6.5.1 Employment Status Immediately After Migration to Switzerland

The multinomial probit models in Table 6.3 analyse the main determinants of the employment status of male and female immigrants immediately after their arrival in Switzerland. The models provide the likelihood of being unemployed or being inactive, rather than being employed, the reference category. The predicted probabilities for men and women by their birthplace and reason for migration are displayed in Figs. 6.1 and 6.2.
Table 6.3

Multinomial probit models for employment status immediately after migration to Switzerland

 

Employment status at arrival

Unemployment

Inactive

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

Birthplace (ref. Germany/Austria)

France

−0.50

0.19

ns.

−0.39

0.20

ns.

−0.63

0.21

**

−0.41

0.19

*

−0.41

0.20

*

−0.66

0.21

***

Italy/Spain

0.21

0.14

***

0.31

0.15

***

0.27

0.16

***

−0.03

0.15

ns.

−0.05

0.16

ns.

−0.02

0.17

ns.

Portugal

0.08

0.17

**

0.01

0.19

ns.

−0.10

0.20

ns.

−0.40

0.20

**

−0.47

0.22

**

−0.58

0.24

***

UK/North America

−0.31

0.16

ns.

0.12

0.18

ns.

−0.12

0.19

ns.

0.19

0.15

*

0.37

0.17

**

0.12

0.18

ns.

India

−0.38

0.18

*

0.11

0.20

ns.

−0.02

0.22

ns.

−0.43

0.18

*

−0.34

0.21

ns.

−0.41

0.22

ns.

South America

0.33

0.17

***

0.48

0.19

***

0.26

0.20

*

0.52

0.17

***

0.54

0.18

***

0.32

0.20

*

Africa

0.86

0.16

***

0.84

0.17

***

0.64

0.18

***

0.62

0.16

***

0.70

0.17

***

0.52

0.18

***

Asia

0.15

0.31

*

0.35

0.33

*

0.22

0.35

ns.

−0.06

0.29

**

−0.21

0.31

**

−0.42

0.32

ns.

Women (ref. Men)

0.17

0.15

**

0.23

0.16

**

−0.04

0.18

ns.

0.56

0.14

***

0.48

0.15

***

0.23

0.17

ns.

Women (ref. Men) *Birthplace (ref. Germany/Austria)

France

0.58

0.27

ns.

0.57

0.28

ns.

0.75

0.29

**

0.47

0.25

ns.

0.40

0.27

ns.

0.52

0.29

ns.

Italy/Spain

0.36

0.21

ns.

0.20

0.22

ns.

0.24

0.23

ns.

0.76

0.20

***

0.75

0.22

**

0.75

0.22

**

Portugal

0.56

0.24

ns.

0.52

0.25

ns.

0.41

0.26

ns.

0.54

0.26

*

0.69

0.28

**

0.61

0.30

ns.

UK/North America

0.71

0.22

**

0.63

0.23

**

0.80

0.25

***

0.70

0.20

***

0.66

0.21

***

0.74

0.22

***

India

1.67

0.25

***

1.39

0.26

***

1.18

0.28

***

2.28

0.24

***

2.15

0.26

***

1.77

0.27

***

South America

0.47

0.23

ns.

0.39

0.24

ns.

0.35

0.25

ns.

0.56

0.21

*

0.53

0.22

ns.

0.49

0.24

ns.

Africa

−0.17

0.24

ns.

−0.02

0.24

ns.

0.03

0.26

ns.

0.18

0.22

ns.

−0.04

0.23

ns.

−0.07

0.24

ns.

Asia

0.40

0.43

ns.

0.14

0.44

ns.

0.18

0.47

ns.

0.56

0.38

ns.

0.70

0.40

ns.

0.79

0.42

ns.

Education (ref. Tertiary)

Secondary

   

0.26

0.07

**

0.09

0.08

ns.

   

0.15

0.07

***

−0.15

0.08

ns.

Primary or lower

   

−0.08

0.12

ns.

−0.28

0.13

***

   

−0.15

0.12

ns.

−0.41

0.13

**

Main Language (ref. Swiss language)

English

   

−0.11

0.09

ns.

−0.06

0.10

ns.

   

0.16

0.08

ns.

0.18

0.09

**

Other

   

0.14

0.10

**

0.12

0.11

*

   

0.30

0.11

**

0.30

0.11

**

Occupational Status Origin (ref. Directive; Managerial)

Self-employed

   

0.22

0.13

**

0.15

0.14

ns.

   

0.16

0.12

***

0.07

0.13

**

Employed, not manager

   

0.38

0.10

***

0.30

0.10

**

   

0.07

0.09

ns.

0.03

0.10

ns.

Other employed

   

0.46

0.15

**

0.34

0.15

ns.

   

0.31

0.14

***

0.22

0.14

**

Unemployed

   

2.03

0.13

***

2.22

0.14

***

   

0.55

0.15

***

0.72

0.16

***

Training/Student

   

0.72

0.14

***

0.81

0.15

***

   

1.88

0.12

***

1.91

0.14

***

Inactive

   

0.90

0.15

***

0.81

0.16

***

   

2.29

0.13

***

2.14

0.13

***

Age at arrival

      

0.03

0.03

ns.

      

−0.17

0.03

***

Age at arrival2

      

0.00

0.00

ns.

      

0.00

0.00

***

Partnership at migration (ref. Not partner at migration)

Partner already in Switzerland

      

0.46

0.10

***

      

0.21

0.09

*

Migrated together

      

0.18

0.10

**

      

−0.05

0.09

ns.

You migrated first/Partner at origin

      

−0.13

0.11

ns.

      

−0.50

0.12

***

Children at migration (ref. Childless)

Children once in Switzerland

      

−0.02

0.09

ns.

      

−0.16

0.09

ns.

Children before migration

      

0.02

0.09

ns.

      

0.44

0.09

***

Sex*Reason for migration (ref. Professional)

Men*Family

      

0.87

0.14

***

      

1.22

0.13

***

Men*Professional + Family

      

0.74

0.16

***

      

0.28

0.20

ns.

Men*Other (Lifestyle…)

      

0.40

0.14

***

      

0.48

0.15

***

Women*Family

      

0.80

0.12

***

      

1.08

0.11

***

Women*Professional + Family

      

0.43

0.16

***

      

0.37

0.16

***

Women*Other (Lifestyle…)

      

0.39

0.15

***

      

0.00

0.15

***

Social network in Switzerland (ref. Not)

      

0.37

0.07

***

      

−0.02

0.07

ns.

Constant

−1.66

0.11

***

−2.43

0.14

***

−3.10

0.55

***

−1.79

0.10

***

−2.43

0.13

***

0.54

0.49

ns.

Number of observations

5823

 

5823

 

5816

 

5823

 

5823

 

5816

 

Log likelihood

−4480.24

 

−4022.66

 

−3704.88

 

−4480.24

 

−4022.66

 

−3704.88

 

Wald Chi2

888.20

***

1598.90

***

1914.91

***

888.20

***

1598.90

***

1914.91

***

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

Statistical significance: ns = non-significant; * p < 0.10; ** p < 0.05; *** p < 0.01

Fig. 6.1

Predictive margins of employment status immediately after migration to Switzerland by origin and gender

Note: Obtained from multinomial models in Table 6.3

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

Fig. 6.2

Predictive margins of employment status immediately after migration to Switzerland by reason for migration and gender

Note: Obtained from multinomial models in Table 6.3

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

The primary conclusion from Table 6.3 is that, compared with German and Austrian immigrants, immigrants from South America and Africa had the lowest probability of being employed after arrival to Switzerland because coefficients for these groups indicated their higher probabilities of unemployment (coefficients β 0.33 and 0.86, respectively) and inactivity (coefficients β 0.52 and 0.62). In the same vein, men from Italy/Spain, Portugal and Asia present a relatively higher likelihood of unemployment, and those born in the UK and North America higher probabilities of inactivity, although differences between them and the reference group of immigrants from Germany and Austria are not large. In contrast, immigrants arrived from France and India present relatively lower risks than German/Austrians do of being unemployed or inactive. Does this disadvantage in employment probabilities for some groups disappear when controlling educational level, primary language, occupation status in origin and the remaining socio-demographic characteristics? The results in successive models indicate that the differences across groups remain almost unaltered, although the highest unemployment probability for the Portuguese and the inactivity of UK/North American immigrants became non-significant. Effectively, although Table 6.3 predicts that the differences across groups decrease after having introduced the control variables, the mentioned differences among immigrants in terms of employment status remain visible (Fig. 6.1). Moreover, the employment probabilities increase for all immigrant men, many of them presenting high percentages of employment (approximately 80% and reaching 90% for French male immigrants). In contrast, men from Africa and South America continue to present the lowest employment levels. Indeed, African male unemployment rises to 20%, and the unemployment rate is 15% for men from South America.

Women are more likely to be unemployed and inactive than men are, and results for the interaction term show that the unemployment is particularly high for women from the UK and North America (coefficient β 0.71) and from India (1.67). In relation to female immigrants from Germany and Austria, the reference group, the remaining immigrant women present a higher probability of inactivity, whereas differences for French and Asian women are no longer significant. For women, Fig. 6.1 clearly indicates that inequalities in employment status across groups are more pronounced than in the case of men, with very low expected probabilities of employment for women from India or South America, who present higher levels of unemployment and inactivity. Women from Spain/Italy, UK/North America and Africa also present low levels of employment (approximately 50%) in comparison with the highest employment levels of women from Germany/Austria, France, Portugal and Asia. Once controls are introduced, the differences by origin are only slightly diminished, and the female employment levels remain strongly heterogeneous across groups, with French women presenting a twice as high percentage of employment as do Indian women.

With respect to the effect of the human capital variables, a higher probability of unemployment and inactivity is observed among secondary educated immigrants, whereas differences between tertiary and primary or lower educated are not significant. Moreover, the results predict better employment probabilities for those who speak English or one of the Swiss languages in comparison to those immigrants who declare another foreign language as their primary language. With respect to the occupation status before migrating to Switzerland, individuals who in their country of origin were directors or managers are more likely to be employed after arrival. Furthermore, as expected, individuals who were not employed before migration present a lower probability of being employed once in Switzerland. This result corroborates that employment status in the country of origin is very likely to continue immediately after arrival (the coefficient β for unemployment is 2.03 for previously unemployed, and the coefficient for inactivity is 1.88 for previous students or in training and 2.29 for inactive from the country of origin). Age at arrival is not significant, whereas having children before migration is associated with higher risk of inactivity. This result is consistent with González-Ferrer (2011), who argues that the temporal sequence of migration and key family lifecycle events might help us in explaining the post-migration employment patterns of migrants.

Moreover, immigrants already having a social network in Switzerland on arrival are more likely to be unemployed or inactive. This point is also true for those immigrants arriving together or after their partners, who are more likely to be seeking a job or inactive. These results are in line with the social networks postulates that having social or family network at the destination is related to having less-urgent economic requirements to find a job after migration and eventually optimize their occupational outcomes. These results could also explain the higher risk of unemployment and inactivity for family-motivated immigrants. However, the fact that inactivity levels are higher when the tied migrant is a woman might also be indicative that gender dispositions remain to be determinant for explaining labour market outcomes after migration. In effect, as seen in Fig. 6.2, post-migration labour insertion for women is more linked with reasons for migration than it is for men.

6.5.2 Employment Status After Settling in Switzerland

Table 6.4 displays the models analysing the employment status of immigrant men and women at the time of the survey. This analysis allows investigating differences by origin in the labour integration considering settling in the Swiss labour market and society, even when the focus is on immigrants relatively recently arrived. Model 1 again indicates heterogeneity in the immigrants’ employment status based on their origin. Effectively, β coefficients for Africans (1.04), South Americans (0.58), Portuguese (0.40), UK/North Americans (0.33) and Asian (0.21) indicated relative higher probabilities of unemployment for them, compared to the reference group. Apart from the Portuguese, these groups also have a higher likelihood of inactivity. In contrast, Italians and Spaniards have improved their relative position in terms of employment status in comparison to their status immediately after arrival in Switzerland (Fig. 6.3). Once we annulled the differences across groups in terms of human capital and occupational status at origin, differences in the probabilities of unemployment in Model 2 only remain significant for Africans. Moreover, this immigrant group presents higher risk of unemployment, as coefficients indicate in Model 3. Likelihood of inactivity remains significantly higher for immigrants from UK/North America (0.29) and from Africa (0.11), although for this last group, differences turn not significant in Model 3. Moreover, the assimilation assumption of a more successful labour insertion for all groups after settlement in the host country appears to be corroborated by higher employment percentages compared with those observed for them after arrival in the host country. This point is consistent with the significant effect of years residing in Switzerland in reducing the individual’s probability of unemployment and inactivity. To summarize, the percentages of employed immigrants displayed in Fig. 6.3 are higher in comparison to those observed after the arrival in Switzerland. Likewise, the differences across origins in terms of employment status are less significant at the time of the interview than are those observed at the beginning of the migratory experience in Switzerland (Fig. 6.1).
Table 6.4

Multinomial probit models for employment status of immigrants in Switzerland at the time of the interview

 

Current employment status

Unemployment

Inactive

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

Birthplace (ref. Germany/Austria)

France

0.03

0.21

ns.

0.01

0.22

ns.

−0.02

0.22

ns.

−0.19

0.24

ns.

−0.22

0.25

ns.

−0.36

0.26

ns.

Italy/Spain

0.07

0.18

ns.

−0.04

0.19

ns.

−0.05

0.20

ns.

−0.11

0.20

ns.

−0.31

0.21

ns.

−0.27

0.22

ns.

Portugal

0.40

0.20

**

0.22

0.21

ns.

0.33

0.22

ns.

−0.30

0.26

ns.

−0.73

0.28

*

−0.74

0.29

*

UK/North America

0.33

0.18

*

0.37

0.20

ns.

0.28

0.20

ns.

0.53

0.18

***

0.29

0.20

***

0.06

0.22

ns.

India

0.08

0.21

ns.

0.10

0.22

ns.

0.13

0.23

ns.

−0.42

0.25

ns.

−0.67

0.28

ns.

−0.82

0.30

ns.

South America

0.58

0.21

**

0.45

0.22

ns.

0.46

0.23

ns.

0.50

0.21

***

0.05

0.23

ns.

−0.07

0.25

ns.

Africa

1.04

0.18

***

0.85

0.19

***

0.86

0.20

***

0.43

0.20

***

0.11

0.22

**

0.06

0.23

ns.

Asia

0.21

0.35

*

0.20

0.37

ns.

0.14

0.38

ns.

0.17

0.34

***

−0.17

0.36

*

−0.38

0.39

ns.

Women (ref. Men)

0.06

0.20

ns.

−0.01

0.20

ns.

−0.34

0.22

*

0.39

0.18

***

0.23

0.19

***

−0.39

0.22

ns.

Women (ref. Men) *Birthplace (ref. Germany/Austria)

France

0.17

0.33

ns.

0.19

0.33

ns.

0.18

0.34

ns.

0.51

0.30

ns.

0.54

0.32

ns.

0.56

0.34

ns.

Italy/Spain

0.71

0.27

**

0.69

0.28

**

0.67

0.28

***

0.83

0.25

ns.

0.78

0.26

ns.

0.77

0.28

ns.

Portugal

−0.22

0.31

ns.

−0.23

0.32

ns.

−0.33

0.33

ns.

0.41

0.32

ns.

0.49

0.34

ns.

0.36

0.36

ns.

UK/North America

0.56

0.26

**

0.59

0.27

**

0.59

0.28

**

0.66

0.23

ns.

0.71

0.25

ns.

0.79

0.26

ns.

India

1.75

0.29

***

1.68

0.29

***

1.45

0.30

***

2.60

0.30

***

2.53

0.32

***

2.21

0.34

***

South America

0.64

0.28

***

0.60

0.28

***

0.43

0.29

***

0.77

0.26

ns.

0.73

0.27

ns.

0.70

0.29

ns.

Africa

0.33

0.27

**

0.38

0.28

***

0.30

0.29

**

0.95

0.26

***

0.84

0.27

**

0.82

0.29

**

Asia

0.29

0.49

ns.

0.27

0.50

ns.

0.38

0.52

ns.

0.87

0.42

ns.

1.17

0.45

ns.

1.40

0.48

ns.

Education (ref. Tertiary)

Secondary

   

0.00

0.09

ns.

−0.05

0.09

ns.

   

0.21

0.13

***

0.07

0.09

ns.

Primary or lower

   

0.03

0.14

ns.

−0.03

0.14

ns.

   

0.12

0.18

*

−0.05

0.14

ns.

Main Language (ref. Swiss language)

English

   

0.04

0.10

ns.

0.03

0.10

ns.

   

0.32

0.10

***

0.36

0.10

***

Other

   

−0.20

0.13

ns.

−0.22

0.13

ns.

   

0.29

0.12

ns.

0.26

0.13

ns.

Not proficient in local language (ref. Proficient)

   

0.05

0.08

ns.

0.01

0.08

ns.

   

0.08

0.08

**

0.08

0.08

*

Education not validated (ref. Swiss educ./Validated/Not necessary)

   

0.70

0.08

***

0.61

0.08

***

   

0.68

0.08

***

0.53

0.08

***

Occupational Status Origin (ref. Directive; Managerial)

Self-employed

   

−0.01

0.14

ns.

−0.03

0.15

ns.

   

0.08

0.13

*

−0.04

0.14

ns.

Employed, not manager

   

0.01

0.11

ns.

0.09

0.11

ns.

   

−0.14

0.10

ns.

−0.02

0.11

ns.

Other employed

   

0.36

0.16

ns.

0.44

0.17

ns.

   

0.27

0.15

ns.

0.31

0.16

ns.

Unemployed

   

0.79

0.14

***

0.90

0.15

***

   

−0.11

0.17

ns.

0.09

0.18

ns.

Training/Student

   

0.04

0.15

*

0.24

0.17

**

   

0.42

0.14

***

0.58

0.16

***

Inactive

   

0.43

0.16

***

0.35

0.16

***

   

1.50

0.13

***

1.39

0.14

***

Age (at the moment of the interview)

      

−0.05

0.04

ns.

      

−0.30

0.03

***

Age2 (at the moment of the interview)

      

0.00

0.00

ns.

      

0.00

0.00

***

Partnership (ref. Not partner)

Partner abroad

      

−0.13

0.16

ns.

      

−0.07

0.17

ns.

Partner first

      

−0.19

0.13

ns.

      

−0.21

0.14

ns.

Migrated together

      

−0.03

0.13

ns.

      

0.12

0.14

*

You migrated first/Alone

      

−0.17

0.12

ns.

      

−0.13

0.13

ns.

Children (ref. Childless)

Children once in Switzerland

      

0.04

0.11

ns.

      

0.74

0.11

***

Children before migration

      

−0.04

0.10

ns.

      

0.49

0.10

***

Sex*Reason for migration (ref. Professional)

Men*Family

      

0.35

0.15

***

      

0.79

0.16

***

Men*Professional + Family

      

0.18

0.21

*

      

−0.23

0.26

ns.

Men*Other (Lifestyle…)

      

0.03

0.15

***

      

0.08

0.18

***

Women*Family

      

1.02

0.13

***

      

1.49

0.12

***

Women*Professional + Family

      

0.57

0.18

***

      

0.63

0.18

***

Women*Other (Lifestyle…)

      

0.31

0.19

**

      

0.64

0.16

***

Years residing in Switzerland

      

−0.04

0.01

***

      

−0.02

0.01

***

Constant

−2.41

0.13

***

−2.66

0.16

***

−1.67

0.75

***

−2.37

0.14

***

−2.66

0.18

***

3.02

0.69

*

Number of observations

5823

5823

5816

5823

5823

5816

Log likelihood

−3335.03

−3094.31

−2872.57

−3335.03

−3094.31

−2872.57

Wald Chi2

823.27

***

1176.42

***

1394.50

***

823.27

***

1176.42

***

1394.50

***

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

Statistical Significance: ns = non-significant; * p < 0.10; ** p < 0.05; *** p < 0.01

Fig. 6.3

Predictive margins of employment status of immigrants at the time of the interview by origin and gender

Note: Obtained from multinomial models in Table 6.4

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

Women are more likely to be economically inactive, and although gender does not affect unemployment likelihood, significantly greater risks are observed in the interaction term for some groups. Expected probabilities displayed in Fig. 6.3 corroborate these findings. Effectively, although immigrant women generally improved their chances of being employed when their employment levels are compared with those after arrival, some of them continue to present high percentages of unemployment and inactivity at the time of data collection. This point is particularly true for Indian women but is also true for immigrant women from the UK/North America, South America, Africa and Asia. In contrast, employment margins for female immigrants from German-speaking countries, France, Italy, Spain and Portugal indicate a high level of employment insertion in the Swiss labour market.

With respect to the effect of the human capital variables, not-significant differences are found by individual educational level. Moreover, the main language or proficiency in the local language is not associated with unemployment, but English-speaking migrants and those not proficient in the host local language are more likely to be inactive than employed. However, a clear effect of educational validation on successful assimilation into the host labour market is found. Finally, unemployment and inactivity risk remain higher after settling in the country for those who were not-employed before migration.

With respect to the other demographic and migratory characteristics, we can mention that age is not significant for unemployment, although it reduces the probability of being inactive. Partnership is not significant for the probability of unemployment but migrating together with the partner is associated with a higher likelihood of inactivity, as is having children. Finally, as was observed in the labour market participation immediately after arrival, family-motivated immigrants, particularly women, present a higher risk of unemployment and inactivity. Figure 6.4 shows that gender differences in employment status related to the reason for migration increased in comparison with those observed in Fig. 6.2 for their status after arrival. This result might indicate that motivation for entrance for men has a temporary effect on labour participation in the host society, whereas for women, it has deeper consequences for employment prospects that remain after settlement in the country.
Fig. 6.4

Predictive margins of employment status of immigrants at the time of the interview by reason for migration and gender

Note: Obtained from multinomial models in Table 6.4

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

Table 6.5 and Fig. 6.5 shows that, independently of their birthplace, immigrant women are more likely than immigrant men are to work part-time at the time of data collection. Moreover, although differences by gender have decreased after covariates were introduced in successive models, the female likelihood of part-time employment remains significantly higher than that of men. Indeed, Fig. 6.5 shows that whereas the expected percentage of part-time employment among immigrant men is situated at approximately 10%, female part-time rises to 30% for many of the origins considered. Furthermore, heterogeneity could be observed across origins even when the differences among groups decrease after considering their human capital, employment characteristics in the country of origin and at the time of the survey, and their family and migratory characteristics. Effectively, a higher likelihood of part-time employment is observed for men and women from South America and Africa and for women from Portugal. In contrast, immigrants from India and France are less likely to work part-time.
Table 6.5

Probit models for part-time employment of immigrants in Switzerland at the time of the interview

 

Model 1

Model 2

Model 3

Model 4

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

B

SE

Sig.

Birthplace (ref. Germany/Austria)

 France

−0.15

0.15

ns.

−0.10

0.16

ns.

−0.10

0.16

ns.

−0.13

0.16

ns.

 Italy/Spain

0.15

0.13

*

0.10

0.14

ns.

0.03

0.14

ns.

0.07

0.14

ns.

 Portugal

0.14

0.14

*

0.08

0.16

ns.

−0.04

0.16

ns.

−0.01

0.17

ns.

 UK/North America

−0.05

0.14

ns.

0.14

0.16

**

0.11

0.16

ns.

0.02

0.16

ns.

 India

−0.32

0.17

**

−0.04

0.19

ns.

−0.12

0.19

ns.

−0.10

0.19

ns.

 South America

0.54

0.16

***

0.46

0.17

***

0.30

0.17

*

0.26

0.17

ns.

 Africa

0.48

0.22

***

0.35

0.24

***

0.21

0.27

*

0.22

0.27

ns.

 Asia

−0.42

0.31

***

−0.31

0.32

ns.

−0.39

0.31

ns.

−0.46

0.32

ns.

Women (ref. Men)

0.95

0.14

***

0.91

0.14

***

0.83

0.14

***

0.56

0.16

***

Women (ref. Men) *Birthplace (ref. Germany/Austria)

 France

−0.01

0.21

ns.

−0.05

0.21

ns.

−0.01

0.21

ns.

−0.09

0.22

ns.

 Italy/Spain

−0.10

0.18

ns.

−0.14

0.18

ns.

−0.14

0.19

ns.

−0.16

0.19

ns.

 Portugal

0.07

0.19

ns.

0.04

0.20

ns.

−0.05

0.20

ns.

−0.14

0.22

ns.

 UK/North America

−0.06

0.19

ns.

−0.10

0.19

ns.

−0.08

0.19

ns.

−0.15

0.20

ns.

 India

0.02

0.25

ns.

−0.12

0.25

ns.

−0.11

0.25

ns.

−0.37

0.26

ns.

 South America

0.02

0.20

ns.

0.03

0.21

ns.

−0.02

0.21

ns.

−0.09

0.22

ns.

 Africa

−0.22

0.33

*

−0.22

0.37

ns.

−0.16

0.37

ns.

−0.30

0.35

ns.

 Asia

−0.22

0.45

ns.

−0.30

0.45

ns.

−0.19

0.42

ns.

−0.20

0.45

ns.

Education (ref. Tertiary)

 Secondary

   

0.17

0.08

***

−0.03

0.11

ns.

−0.12

0.11

*

 Primary or lower

   

0.18

0.11

*

−0.13

0.14

ns.

−0.27

0.15

**

Main Language (ref. Swiss language)

 English

   

−0.21

0.11

***

−0.16

0.11

**

−0.12

0.11

*

 Other

   

−0.12

0.10

ns.

−0.13

0.10

ns.

−0.14

0.11

ns.

Not proficient in local language (ref. Proficient)

   

−0.01

0.07

ns.

−0.01

0.07

ns.

−0.02

0.07

ns.

Education not validated (ref. Swiss educ./Validated/Not necessary)

   

0.25

0.09

***

0.10

0.08

**

0.09

0.09

*

Employment Status Origin (ref. Employed full time)

 Employed part time

   

1.10

0.10

***

1.02

0.10

***

1.07

0.10

***

 Unemployed

   

0.30

0.11

***

0.24

0.11

*

0.36

0.11

**

 Training/Student

   

0.11

0.12

*

0.11

0.13

ns.

0.25

0.13

***

 Looking after home or family

   

1.14

0.24

***

1.02

0.23

***

0.92

0.23

***

 Disabled/Retired/Pensioner/Other non-employed

   

0.36

0.18

**

0.34

0.18

**

0.36

0.18

**

Job Offer before migrating (Ref. Yes)

      

0.28

0.07

***

0.15

0.07

***

Occupation (ref. Directive, Managerial)

 Self-employed

      

0.61

0.14

***

0.62

0.14

***

 High-Skilled employee

      

0.13

0.11

***

0.17

0.12

***

 Skilled employee

      

0.36

0.13

***

0.36

0.13

***

 Unskilled employee

      

0.65

0.15

***

0.62

0.15

***

Overqualified (Ref. Not)

      

0.10

0.08

**

0.10

0.08

*

Age (at the moment of the interview)

         

−0.03

0.03

*

Age2 (at the moment of the interview)

         

0.00

0.00

**

Partnership (ref. Not partner)

 Partner abroad

         

−0.03

0.14

ns.

 Partner first

         

0.05

0.12

ns.

 Migrated together

         

−0.01

0.12

ns.

 You migrated first/Alone

         

0.00

0.11

ns.

Sex*Children (ref. Childless)

 Men*Children once in Switzerland

         

−0.17

0.15

ns.

 Men*Children before migration

         

0.02

0.12

ns.

 Women*Children once in Switzerland

         

0.81

0.14

***

 Women*Children before migration

         

0.50

0.13

***

Sex*Reason for migration (ref. Professional)

 Men*Family

         

0.45

0.15

***

 Men*Professional + Family

         

0.34

0.18

**

 Men*Other (Lifestyle…)

         

0.33

0.13

**

 Women*Family

         

0.49

0.12

***

 Women*Professional + Family

         

0.19

0.17

ns.

 Women*Other (Lifestyle…)

         

0.03

0.15

ns.

Years residing in Switzerland

         

0.01

0.01

ns.

Constant

−1.36

0.10

***

−1.64

0.12

***

−1.87

0.14

***

−1.59

0.71

**

Number of observations

4609

4609

4609

4603

Log likelihood

−2047.38

−1879.79

−1772.04

−1691.16

LR Chi2

621.91

***

957.08

***

1172.58

***

1331.63

***

Pseudo R2

0.13

0.20

0.25

0.28

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

Statistical Significance: ns = non-significant; * p < 0.10; ** p < 0.05; *** p < 0.01

Fig. 6.5

Predictive margins of part-time employment of immigrants at the time of the interview by origin and gender

Note: Obtained from probit models in Table 6.5

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

Likelihood of part-time employment is negatively correlated with educational level. In the same vein, immigrants who did not validate their education obtained abroad, overqualified employees and those without a job offer before migration present a higher probability of working part-time. Swiss language speakers are the most likely to be working part-time, although proficiency in a local language is not associated with the outcome. As expected, immigrants in part-time employment or those looking after their families before migration present higher likelihood of part-time employment. In relation to those holding director or manager roles at the time of the interview, self-employed and unskilled employees are the most likely to work part-time. Furthermore, age and years of residence in Switzerland are not significant in predicting part-time employment.

Finally, whereas partnership and the couple’s migratory process are not associated with our outcome, having children is a strong determinant of part-time employment for women, particularly for mothers who had their children after migration (Fig. 6.6). In contrast, differences are not observed between fathers and childless men in their probability in terms of working part-time. Finally, likelihood of part-time employment is higher among family-motivated migrants than among profession-related migrants. Moreover, gendered patterns also appear in relation to the effect of the motivation for migration on part-time employment in Switzerland. Indeed, Fig. 6.7 clearly shows that the likelihood of working part-time is more linked with reason for migration for women than for men.
Fig. 6.6

Predictive margins of part-time employment of immigrants at the time of the interview by children and gender

Note: Obtained from multinomial models in Table 6.5

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

Fig. 6.7

Predictive margins of part-time employment of immigrants at the time of the interview by reason for migration and gender, obtained from multinomial models

Note: Obtained from multinomial models in Table 6.5

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

6.6 Conclusions and Discussion

Despite the short time span involved in the length of residence of immigrants considered in this research, a median of 5 years since arrival, results are consistent with the assimilation hypothesis because they point to an improvement in terms of labour market insertion during the process of settlement in Switzerland. Nevertheless, significant differences have emerged by gender that broadly justify the decision to include the gender perspective in the analysis and in the interpretation of the results. The first aim was to analyse dynamics in terms of employment status at the time of migration and during the process of settling in Switzerland by origin and gender. In the male case, results corroborate the influence of the length of residence in the country and the adaptation to the host labour market in reducing the individual’s risk of unemployment or inactivity. Consequently, employment probabilities at the time of the survey are clearly higher for all groups of immigrants than they are immediately after migration. Moreover, the investigation was particularly interested in analysing whether the differences by origin in the immigrants’ employment prospects in Switzerland resulted from the differences in terms of human capital composition among them. Or if, on the contrary, disadvantages in the labour market integration of some groups persist after the human capital differences are controlled for. In this respect, segmentation hypothesis could explain the heterogeneity in the level of employment of immigrants recently arrived, and the fact that differences by origin persist even after controlling for their human capital, their occupational status in the country of origin, and their socio-demographic and migratory covariates. Therefore, results indicate some degree of segmented labour insertion of recent migrants upon arrival in Switzerland. In contrast, no differences across groups were obtained in terms of employment status after settlement in the country, in line with human capital and assimilation postulates. Only African immigrants continue to present a significantly higher risk of unemployment, regardless of their education and their level of assimilation into the host labour market.

The evolution of the employment status of immigrant women also points to some degree of progression in their employment probabilities after settlement in the country; therefore, assimilation postulates are also corroborated for them. However, in comparison with men, they are, in general, more likely to be unemployed or inactive. Only women from Germany/Austria, France and Portugal present levels of employment similar to their male counterparts. Moreover, as results show, a meaningful proportion of these women are working part-time. In contrast, significant percentages of inactivity among some groups of female immigrants, such as among those from the UK/North America, India, South America, and Africa, clearly indicated lower post-migration participation in the labour force for these women. Furthermore, although differences in terms of employment levels among immigrant women decrease after controlling for their human capital characteristics, employment status in their countries of origin, family circumstances or motives for migration, heterogeneity across groups in terms of labour force participation remain in the female case.

Finally, the richness of the information in the data source in terms of the migration process allowed us to corroborate that post-migration employment is lower for tied migrants and family-motivated migrants. Even when the reason for migration has the same effect for men and women, margins obtained indicated that the employment prospects of women are more determined by the reason for migration than are those of men. Furthermore, based on the results obtained, family-motivated migration has only temporary effects on labour market integration of male migrants, whereas it harms employment prospects for women more permanently. In fact, women’s inactivity levels and part-time employment remain very high after settlement in Switzerland.

The chapter yielded evidence of the Migration-Mobility Nexus because it considers migration and migrant labour market integration mobility processes that start in the society of origin and evolve during settlement in the host society. Although the study contributes to enhancing the understanding of the decisions immigrants make to attain a successful incorporation in the host country labour market, it is not without limitations. First, a limiting feature of this study is that the immigrants have resided for such a short period in Switzerland, a median of 5 years. Although biases due to emigration, death or abandonment of the labour market increase with the length of time considered, availability of longitudinal panel data following individuals through a longer period would provide a more accurate understanding of the immigrants’ adjustment process. Moreover, although analysis corroborated that immigration to Switzerland is, in general, an advantageous experience in terms of employment prospects, further research is needed to better understand other aspects of immigrants’ labour market integration. For example, descriptive analysis showed that less-advantaged immigrant groups in terms of human capital and employment status at origin are precisely those expressing higher levels of progression in their professional situation in Switzerland in comparison to that in the country of origin, despite their less favourable position in the Swiss occupational scale. Therefore, more-accurate analysis is necessary to corroborate the neoclassical argument that the subjective measure of professional success after migration is caused by differences in the rate of return to human capital between home and destination countries. Given the economic prosperity, job opportunities and high wage standards of Switzerland, the country would be a very special case to study. However, regardless of the richness of the information included in the Migration-Mobility Survey, one of its limitations is the absence of retrospective information in relation to immigrants’ wages and positions within an international index of occupational scale. The availability of this information would facilitate a cross-country comparison and a quantification of immigrants’ occupational and earnings trajectories. Finally, the prospect of immigrants returning to their source country or of permanent settlement in Switzerland is, in the author’s opinion, determined not only by the success of their insertion in the host labour market but also by individuals’ ability to be truly integrated into the host society. As stated in Vidal-Coso and Ortega-Rivera (2017), this point is particularly relevant for Switzerland, in which labour integration is facilitated by immigration policies, whereas societal and political integration remain restricted. As in many other countries, economic ground in Switzerland is strongly dependent upon not only attracting but also retaining skilled workers. In the same vein, challenges faced by women to combine family life and paid work should be carefully addressed.

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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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