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Who Feels Disadvantaged? Reporting Discrimination in Surveys

  • Daniel AuerEmail author
  • Didier Ruedin
Open Access
Chapter
Part of the IMISCOE Research Series book series (IMIS)

Abstract

In this chapter, we seek to shed light on the mechanisms of perceived discrimination: Who, among recent immigrants, is more likely to feel discriminated against and report it when asked in a survey? Social scientists typically define discrimination as an observable and unjust difference in the treatment of distinct groups. To personally feel discriminated against, people must be aware of the differential treatment and perceive it as unjust. We show that reporting discrimination when asked in a survey depends substantially upon individual traits, including aspects that shape whether discrimination is accepted and whether immigrants feel attached to the host society. Although respondents report less discrimination if their job situation has improved after migration, people more likely report discrimination when they originate from countries in which the national legislature represents ethnic minority groups relatively well. Earlier difficulties related to the migration process and the lack of supporting networks continue to affect the perception of unfair treatment. Moreover, we show that individuals distinguish to a surprising degree between discrimination in and outside the work environment. For instance, when they are proficient in the local language, respondents often report discrimination in the workplace but not in a public environment. This distinction between discrimination in the workplace and discrimination in public also depends strongly upon the immigrant’s origin. We conclude that contemporary individual-level measures and policy recommendations merely approximate discriminatory patterns; we urge future research to consider factors that affect individual perception of discrimination.

Keywords

Perceived discrimination Immigration Workplace Public sphere Ethnic minority Switzerland 

9.1 Introduction

In a world increasingly characterized by growing ethnic diversity, questions of inter-group relationships and social cohesion find their way into the political debate (Green-Pedersen and Otjes 2017; Van der Brug et al. 2015). When individuals are treated differently along ethnic, cultural or legal lines depending upon their group membership, discrimination is a frequently used and arguably often correct label. However, between and even within disciplines of social science, there is little agreement on what exactly discrimination is or, moreover, on how it can be measured. Cleavages in the understanding of discrimination become particularly visible when the discriminating person can provide a plausible justification for their action (e.g., blame shifting; see Campbell et al. (2012)) or when victims of discrimination are either unaware of their disadvantage or do not feel disadvantaged at all. In this chapter, we focus on the perception of discrimination because if policymakers try to maximize individual well-being, they should find it valuable to know who feels discriminated against and under what circumstances. Moreover, knowledge about the drivers of perceived discrimination and the reporting of discrimination when asked in a survey enables policymakers to implement tailored means to diminish perceptions of unfair treatment and discrimination. Indeed, we argue that perceptions of discrimination are often more important for the well-being of people than ‘objective’ discrimination, although the two will be related to one another.

For discrimination to occur, we need at least two actors. One of these actors unfairly treats the other based on an irrelevant criterion such as ethnicity, country of origin, or gender. Theories of justice provide answers concerning what constitutes unfair treatment and what criteria can be considered irrelevant (e.g., Rawls 1999; Sen 2009). For instance, Rawls introduces the concept of the ‘veil of ignorance’ to highlight that criteria that have no bearing on people properly performing their job cannot be considered just – assuming a hiring situation in this case. For instance, the skin colour of a worker has no bearing on the productivity of the worker, so selecting workers based on skin colour is unjust. In contrast, selecting manual workers on their dexterity is just because it affects the productivity of the worker. In other words, if a criterion for selecting a worker is irrelevant for that person’s productivity, we can say that the differential treatment based on that criterion is unfair and discrimination occurs. Objectively speaking, discrimination does not require witting action; nor does the person affected have to perceive it as unfair. In this chapter, we present a non-exhaustive list of (unintentional) factors that influence individual awareness of discrimination or the perception of unequal treatment as unjust. This ostensible unawareness of discrimination has important consequences for discrimination research and possible policy responses to discriminatory behaviour because nescience about being the victim of discrimination blurs our understanding about the extent of unfair treatment.

At the same time, we argue that social scientists and policymakers should pay attention to perceptions of discrimination because these are linked to well-being, poor health, and ultimately social cohesion (Versey and Curtin 2016; Simona et al. 2015; Hanefeld et al. 2017). We therefore create a model to predict the individual propensity to report discrimination when asked in a survey. Conceptually, people can feel discriminated against in situations in which there is no objective discrimination – consider a situation in which the treatment of different groups is objectively the same – or they might not feel discriminated against in situations in which there is objective discrimination. The latter can occur when individuals do not perceive differences in treatment or do not consider these differences unjust, for instance, because they have internalized social roles that naturalize such differences.

We argue and demonstrate that, among recent immigrants in Switzerland, the perception and reporting of discrimination relates to reported difficulties during immigration, how immigrants succeed economically, how ethnic minority groups are included in politics in the country of origin, and other individual characteristics. We show that immigrants differentiate between discrimination at work and in public, indicating that immigrants perceive greater discrimination when it comes unexpectedly and that reporting discrimination in a survey is the result of various individual circumstances rather than the haphazard response to a yes-or-no question. We thereby empirically picture aspects of the Migration-Mobility Nexus (compare Chap.  1 in this volume) by highlighting that the exclusionary logic of the societal sphere is substantially influenced by the inclusionary logic of the economic sphere. In other words, whether discriminatory treatment of immigrants becomes visible depends in part upon an implicit trade-off with economic gains. Moreover, we stress that societal exclusion along the lines of the Swiss dual regime of migration and mobility (see Sect. 9.2) is decisively blurred by individual contexts. Although studies have shown that the dual immigration regime distinguishing between EU/EFTA and non-EU/EFTA nationals provides a powerful proxy for societal exclusion (e.g., Auer and Fossati 2018), we stress that individual perception of attachment and welcoming can mitigate or even counter statements about discrimination that appear to be valid on the aggregate level.

9.2 Context: Immigration to Switzerland

Despite a trend towards more-expansive policies (Ruedin et al. 2015), Switzerland has relatively restrictive naturalization policies that contribute to a high share of foreign citizens. Traditionally, immigration was regulated restrictively as a function of the needs of the economy (Piguet 2004). After World War II, Switzerland experienced a substantive growth in the immigrant population, helped by a booming economy and guest worker recruitment in Italy, Spain, Portugal, Turkey, and Yugoslavia. To prevent workers from settling, the guest-worker programmes were designed to rotate workers. Increasing competition for these workers with other Western European countries, combined with growing pressure to respect worker rights by civil society and international organizations, however, led to family reunification and settlement of a growing number of immigrants (e.g., Ruedin 2015). After a gradual loosening of its immigration policies, Switzerland introduced a tiered labour market model in the early 1990s (Becker et al. 2008). Swiss immigration policy sought to balance the diversification of migration due to emerging trends of globalization with the highly emotional public debate around Überfremdung – a “fear of domination by foreign influences”, implying too many foreigners and foreigners who are “too foreign” in the sense of cultural distance. Immigrants from countries with perceived cultural closeness (Western Europe, USA, Canada, Australia and New Zealand) were granted preferential access. In the late 1990s, the wars in (former) Yugoslavia brought an unprecedented number of refugees to Switzerland, partly an indirect consequence of existing ties through guest-worker programmes. The “three-circles” system was reduced to two tiers (European Union vs. rest of the world) in 2002, which led to high inflows from European countries, particularly from Portugal and Germany (Becker et al. 2008).1 The presence of immigrants – foreign citizens and their children – has long been politicized (Ruedin and D’Amato 2015). As in other countries, a significant part of the population has negative attitudes towards foreigners (Pecoraro and Ruedin 2016). However, Switzerland also has a long tradition of human rights and justice as exemplified by the International Red Cross (Ruedin and D’Amato 2015).

This contradictory understanding as a country inherently shaped by and being open towards immigration on the one hand and a subordination of restrictive immigration policies to an economic rationale on the other hand make Switzerland an interesting place to conduct research on discrimination. In the public sphere, racism is commonplace (Ruedin 2015; Efionayi-Mäder and Ruedin 2017). Exploiting a natural-experimental setting in which municipalities voted over naturalization applications, Hainmueller and Hangartner (2013) show that immigrants from former Yugoslavia and Turkey had a substantially lower likelihood of being granted Swiss citizenship than equivalent immigrants who originated from northern or western European countries. Moreover, Switzerland is no exception concerning discrimination against immigrants in the labour market, as has been shown in a recent meta-analysis of experimental evidence (Zschirnt and Ruedin 2016). The systematic disadvantage of immigrants in the hiring process (Fibbi et al. 2006 and Zschirnt, E. (2018, March 23). Ethnic discrimination in the Swiss (German) labour market – first results from a correspondence test. Presented at the NCCR Research Day #5, Neuchâtel) is also reflected in sharp native-immigrant wage differentials (Henneberger and Sousa-Poza 1998; Iseni et al. 2014) and longer unemployment durations of foreigners (Auer et al. 2018). Recently, Auer and Liechti argued in their presentation What does effect-heterogeneity of active labour market programmes reveal about discrimination? at the annual IMISCOE Conference 2018 in Barcelona that statistical discrimination by employers dominates over taste-based discrimination by showing that immigrants’ potential benefits from participation in active labour market programmes exceeds those of natives. According to the authors, such a finding can only materialize if employers allow their stereotypes to be influenced. Such a “pragmatic approach” towards discrimination is in line with Switzerland’s largely economy-driven immigration policies and reflects the evidence of surprisingly deliberate statistical discrimination reported in the social domain in Switzerland (Fibbi et al. 2018).

9.3 Theoretical Considerations

There is extensive literature on ethnic discrimination and a clear recognition that perceptions of discrimination do not necessarily match “objective” measures of discrimination (Blank et al. 2004). However, no agreed-upon understanding exists of why some individuals feel more discriminated against than others do or are more likely to report discrimination in a survey when asked. Existing research on perceptions of discrimination has highlighted variances within ethnic groups but has provided mixed evidence with respect to individual socio-demographic variables (Zainiddinov 2016; Alanya et al. 2017; Flores 2015; McGinnity and Gijsberts 2016; Hopkins et al. 2016). Zainiddinov (2016) highlights that most existing research on perceived discrimination focusses on Black Americans, although more-recent research also includes other minority groups, notably Hispanics and Asians. People with lower socio-economic status tend to report more discrimination, as do older respondents. The evidence for gender differences is mixed, with most studies finding no substantive differences. The association between levels of education or income and perceived discrimination has been reported to be positive, negative, or indeed curvilinear – which we take as inconclusive evidence, as does Zainiddinov (2016). Alanya et al. (2017) examine perceived discrimination in several Western European cities, highlighting that the experience of immigrant integration might also matter, although they find substantive differences between the cities examined. In this chapter, we expand on this literature by highlighting plausible drivers of such perceptions, although we leave the development of a full theory for future research (compare Swedberg 2014). In our view, such a theory should start with the threat framework generally used as an explanation for intercultural relationships and attitudes towards foreigners (Blumer 1958; Bobo and Hutchings 1996). In the threat framework, immigrants are perceived as unwanted competitors for scarce resources such as jobs or social benefits. In a zero-sum understanding of these resources, the presence and size of different groups in society is perceived as a threat and can lead to discrimination. Although notions of threat are usually applied to the majority population, we focus here on immigrants at the receiving end.

In virtually all societies, there are accounts of “objective” discrimination being overseen, ignored, or “justified” by individuals. That is, people do not identify inequality in the first place or do not perceive unequal treatment as discriminatory ranging from gender discrimination to taxation justice, access to goods and services, hiring, or less materialistic aspects such as active participation of persons with special needs in sports clubs. We argue that the discrimination against immigrants follows the same mechanism. Although immigrants and their descendants are often treated to their disadvantage in labour markets or elsewhere (Heath and Cheung 2007; Neumark 2016; Zschirnt and Ruedin 2016; Oswald et al. 2013), their individual perception of discrimination can deviate substantially from objective measures (Hopkins et al. 2016). We argue that discrimination can be analysed as both an objective, systematic unfair differential treatment of two or more groups and as a subjective perception of such a treatment, in which case the latter is influenced by individual characteristics within three key areas among others. We explicitly leave the influence of context and the interaction between context and individual characteristics to future research. Hopkins et al. (2016) demonstrate that immigrants’ perceptions of discrimination in the USA vary very little across localities, suggesting that the influence of context alone can be more intricate than a direct relationship – or indeed that individual-level factors such as we examine in this chapter are more important for perceptions of discrimination and for reporting such perceptions in a survey.

First, we expect that people who are more likely to be discriminated against (objectively) are more likely to report the experience of discrimination in a survey when asked. This area is close to conventional residual studies that highlight the unexplained gap, for instance in labour market outcomes, after controlling for a number of socio-economic characteristics (e.g., Auer et al. 2017; Ballarino and Panichella 2015). Although individual traits such as country of origin affect the propensity to be exposed to discriminatory behaviour, these objective (read: general) drivers of discrimination are external – not within the scope of individuals to change. This point is likely most apparent in the case of racial and ethnic differences such as skin colour (Zainiddinov 2016).

Second, because of material gains and improved labour market outcomes, people might accept a certain degree of discrimination. Here we assume that some immigrants might come prepared to face unjust treatment and are willing to tolerate the unpleasant experience as part of the “costs” of immigration that are compensated for by the (financial) benefits of immigration. Similarly, immigrants might accept discrimination as a cost they pay so that their children can reap the benefits of immigration. It is also possible that immigrants internalize roles in which differential treatment appears “natural” and therefore accept that treatment. To an extent, this internalization of social roles can be considered a cost to immigrants. Because long-term strategies and hopes of a better future are difficult to capture, we focus here on more-immediate gains. It follows that immigrants whose labour market situation has improved after migration can be expected to be more willing to accept differential treatment and are hence less likely to report discrimination in a survey. This argument is in line with findings by Zainiddinov (2016) that older Muslims in the United States are less likely to report discrimination; they might be “tolerating” unfair treatment and accept whatever is necessary to the extent that their children born in the country are treated equally and benefit from migration to the United States. Importantly, acceptance implies some form of trade-off or bargain between current or future economic (or social) benefits at the expense of potentially unfair treatment. Thus, not perceiving or reporting discrimination because a person accepts or tolerates it stands in opposition to the subsequent expectation according to which individuals can develop a genuinely positive feeling towards the host country without any trade-off attached.

Third, we expect attachment to the country of destination in general to be associated with the likelihood of reporting discrimination when asked in a survey. People who are positive about the country of destination arguably possess a higher “tolerance” concerning critical aspects about the society of the host country and, hence, are less likely to perceive treatment as unjust and discriminatory. In contrast to the influence of acceptance as presented above, attachment to the country of destination does not necessarily entail economic gains. Rather, people can feel attached to or identify with a country of destination if their habitus and worldview are compatible with the dominant cultural aspects of the destination country. This point suggests that socialization in the country of origin shapes how the situation in the country of destination is perceived and hence how discrimination is perceived and reported when investigated in a survey. However, attachment and discrimination are likely to be interdependent; people who are discriminated against are less likely to feel attached to the country of destination, but those who feel attached to the country of destination are less likely to perceive acts as discriminatory and report discrimination in a survey. It is inherently difficult to assess which of the two concepts drives the other, particularly with cross-sectional data. To (partly) overcome this issue, as we will elaborate below, we differentiate between two types of attachment proxies; on the one hand, there is straightforward reporting of feelings of attachment. On the other hand, we introduce variables that are associated with feelings of attachment but that capture situations prior to or during the migration process, including socialization in the country of origin. By definition, these elements are not or are less likely to be confounded with possible subsequent incidents of discrimination in the host country and, hence, are less prone to bias the relationship between attachment and reporting of discrimination. In other words, positive or negative experiences during the migration process should be related to the reporting of unfair treatment in the host country without being biased by an already developed feeling of attachment that could blur the perception of discrimination or by a form of alienation from the host country due to incidents of discrimination.

9.4 Data and Method

To examine who is more likely to feel discriminated against and how potential drivers affect perceptions of discrimination, we use new data from the Migration-Mobility Survey that cover detailed information on socio-economic traits, subjective well-being, and the migration experience of approximately 6000 recent immigrants in Switzerland (Migration-Mobility Survey 2016; see Chap.  2). We use three direct questions about the experience of discrimination in different situations. A general measure of perceived discrimination asks, “Have you experienced situations of prejudice or discrimination in Switzerland in the last 24 months?” Two further questions ask specifically about experienced discrimination in the workplace (including education facilities) or during leisure time and in the more general public sphere.2

We use a range of predictor variables to capture the three stated mechanisms for differences in perceptions or reporting discrimination: drivers, acceptance, and attachment. These variables are presented in Table 9.1; the question wording can be found in Table 9.4 in the Appendix.
Table 9.1

Predictor variables and expectations

Category

Variable

Expectation: Reported discrimination ...

DRIVERS

no local language skills

...increases without Swiss language skills

 

born outside the EU

...increases with cultural distance

 

lack of private network

...increases without private network

 

lack of professional network

...increases without professional network

ACCEPTANCE

improved econ. situation

...decreases with improvement of job situation

ATTACHMENT

exposure to diversity

...decreases with lower minority representation

 

migration difficulties

...decreases with unproblematic migration

 

satisfaction about migration

...decreases with satisfaction about mig. decision

 

attachment to destination

...decreases with attachment to Switzerland

Initially, as shown in Table 9.1, individual propensity to being discriminated against varies with group membership (e.g., Auer et al. 2018; Ebner and Helbling 2016). We expect that individuals who belong to groups objectively more often discriminated against report discrimination more often. Such drivers typically include local language proficiency (depending upon the Swiss language region), which provides a key driver of integration success and is expected to reduce exposure to discrimination (Föbker and Imani 2017; for the labour market, see Auer 2018). Being born within the European Union has been shown to be a powerful approximation for “cultural proximity” (Ruedin 2018b). At the same time, existing networks among family and friends or among work colleagues have been shown to be beneficial in various ways, ranging from increased well-being (Portela et al. 2013) to higher labour-market access probability (Bonoli and Turtschi 2015) and less exposure to discrimination (Jasinskaja-Lahti et al. 2006).3 Moreover, we consider geographical origin by including region-fixed effects in all models. We thereby consider the literature on ethnic hierarchies or ethnic rankings based on socio-economic differences and ethnic traits among immigrants (Hagendoorn 1995).

Drivers of discrimination are usually observable by other people, for instance in job applications, during administrative dealings but also in private everyday interactions. Hence, they have the potential to generally increase the propensity to be discriminated against. If we assume that people who are unfairly treated on average also have a higher propensity to report such treatment, we must control for these individual characteristics in our statistical models to render the remaining – unobservable – traits meaningful determinants of perceived discrimination. One issue with testing what drives individual perception of discrimination is the direction of the relationship. Does a happy immigrant experience less discrimination or is a less-discriminated-against immigrant happier? We try to minimize this bias by focussing on aspects that occurred prior to migrating or during migration to Switzerland in our main analysis. Bearing in mind the possibility of a biased reflection of past events, which is inherent to all survey information, reported discrimination can only be the result of these aspects and not vice versa.

Hence, we capture acceptance of discrimination with changes in individual economic opportunities compared with the situation in the country of origin. We argue that if the job situation of a person has improved since migrating to Switzerland, incidents of discrimination are not perceived as such as often (or perceived as less severe) because of the compensating ability of economic success. The same mechanism applies for feeling right for a job in terms of qualification.

Although accepting differential treatment is the result of a biased perception due to (material) gains, elements of attachment can reduce individual propensity to perceive and report discrimination. We argue that a lack of past socialization in the country of origin in terms of ethno-cultural diversity (approximated by low representation of ethnic minority groups in the national legislature; Ruedin 2009), decreases discrimination perceptions. The intuition is that if individuals have been confronted with diversity, the promotion of equal opportunities or anti-discrimination efforts in their country of origin, they are likely to have established some form of awareness of these issues (Ziller 2014). Subsequently, they should be more sensitive in terms of noticing discrimination in the country of destination. Conversely, the absence of adequate minority representation in the country of origin increases the propensity to be unaware or ignorant of issues of inequality and discrimination. Furthermore, a “smooth” migration process, that is, the reported absence of issues when entering Switzerland and when registering at various immigration offices, can shape the overall notion about feeling “welcome” in the host country. If this statement is true, individuals should identify more with Swiss society and perceive discriminatory patterns less often or as less “severe”. We measure this aspect with a variable on satisfaction with the decision to migrate to Switzerland. Finally, we consider three aspects of potentially very strong confounders of discrimination perception. As we will briefly elaborate below, however, these aspects should be interpreted with special caution due to a direct reciprocal relationship with experienced discrimination. We capture whether the person feels attached to Switzerland. Intuitively, someone who lacks a certain subjective feeling of attachment is more likely to perceive situations as particularly inconvenient and, hence, reports discrimination more often. Conversely, attachment to Switzerland creates a ‘blind spot’ with respect to discrimination.

Table 9.2 presents descriptive statistics of the outcome and predictor variables. Overall, 35% of the recently arrived immigrants in Switzerland report having experienced situations of prejudice or discrimination in Switzerland in the preceding 2 years. The reported discrimination at work and in public situations is similar. With respect to the predictor variables, more than one-half of the respondents do not speak the local language. Approximately one-half of the immigrants come from outside the EU, and they often report neither personal (73%) nor professional (60%) network links to Switzerland prior to arriving (see Table 9.4 in the Appendix for question wordings and operationalization). On average, respondents are very satisfied with their decision to migrate (8.3 of 10). At the same time, feelings of attachment to Switzerland are largely positive but less pronounced (4.8 of 7). These figures correspond roughly with the findings by Geurts and Lubbers (2017) that approximately 60% of immigrants in the Netherlands are positive about their migration decision and intend to stay permanently.
Table 9.2

Descriptive Statistics

 

Mean

Min

Max

Median

DISCRIMINATED (GENERAL)

0.35

0

1

0

DISCRIMINATED (WORK)

0.17

0

1

0

DISCRIMINATED (PUBLIC)

0.15

0

1

0

FEMALE

0.44

0

1

0

AGE AT ARRIVAL

34.71

18

64

33

YEARS OF STAY

5.39

1

11

5

NO LOCAL SWISS LANGUAGE SKILLS

0.52

0

1

1

BORN OUTSIDE THE EU

0.34

0

1

1

LESS EDUCATION

0.48

0

1

0

UNEMPLOYED

0.52

0

1

1

NO PRIVATE NETWORK

0.74

0

1

1

NO PROFESSIONAL NETWORK

0.64

0

1

1

IMPROVED EMPLOYMENT SITUATIONa

3.96

1

5

4

QUALIFICATION MATCH

0.76

0

1

1

ASSEMBLY REPRESENTATIONb

0.95

0.689

0.999

0.963

MIGRATION DIFFICULTIESc

2.41

0

7

2.5

SATISFACTION ABOUT MIGRATIONd

8.25

0

10

9

ATTACHMENT TO SWITZERLANDe

4.64

0

7

5

ORIGIN: EUROPE

0.65

   

ORIGIN: N-AMERICA

0.10

   

ORIGIN: S-AMERICA

0.09

   

ORIGIN: ASIA

0.10

   

ORIGIN: W-AFRICA

0.07

   

OBSERVATIONS

5189

   

Note: The weighted mean, minimum, maximum, and median for each variable are given. For binary variables such as ‘Female’, the mean corresponds to the percentage

a‘worsened substantially’ (=1) to ‘improved substantially’ (=5)

b‘Assembly representation’ is 1 if the share of ethnic groups in the national legislature is perfectly proportional to the share of ethnic groups in the population, and 0 if it is perfectly disproportional

c‘very problematic’ (=0) to ‘not problematic at all’ (=7)

d‘not satisfied’ (=0) to ‘completely satisfied’ (=10)

e‘no feeling’ (=0) to ‘strong feeling’ (=7)

Source: Migration-Mobility Survey 2016. Weighted results

In terms of analytical strategy, we predict the likelihood of reporting discrimination in general, at work, and during public activities using three outcome variables in separate models. We depart from a baseline logistic regression model that captures fundamental individual drivers and includes fixed effects for the geographical origin. Additionally, we account for certain individual traits that might affect both the perception of discrimination and actually being discriminated against. These baseline characteristics include gender, age at arrival, the duration of stay in the host country, and employment status (see, for instance, Zainiddinov 2016 and Alanya et al. 2017). Subsequently, we add variables to capture the acceptance mechanisms in the model. We add a battery of attachment variables that influence perception but are more sensitive with respect to the potential issue of reverse causality. Ex-post empirical analyses of survey responses are always biased by subjective interpretations of events, which is particularly problematic for establishing a (causal) mechanism. Does an immigrant who feels attached to Switzerland report less discrimination, or does an immigrant who is less discriminated against feel more attached to the country? We try to circumvent this reverse pathway issue by focussing on evaluations of past events. For instance, at the time when someone decided to migrate to Switzerland for employment, he or she was not exposed to discrimination by Swiss society. Hence, it is safe to assume that a certain response might be biased by subjective interpretation but not by our factor of interest, that is, discrimination. This assumption should hold for aspects with less clear-cut temporal distinction, such as reported issues during the migration process. It is possible that the newly arrived person has previously been exposed to discrimination; however, we consider this probability small and, not least, its effect limited given that respondents on average have remained in Switzerland more than 5 years. For the parts of the variables capturing attachment, however, the direction of the relationship is less clear. Therefore, we interpret these factors with special caution.

9.5 Findings

In this section, we highlight the most important findings based on the logistic regression models presented in Table 9.3. If not stated otherwise, we refer to the full regression models on discrimination in general, at work, and in public situations presented in Table 9.3.4 Although most variables thought to be drivers were found to be weak predictors for reporting discrimination, the geographical origin indicates a high relevance of ethnic traits, with different statistical effects between discrimination at work or in public situations. Elements of both acceptance and attachment play a substantial role with respect to perceiving differential treatment as unfair.
Table 9.3

Regression results

 

Discrimination (general)

Discrimination (work)

Discrimination (public)

Female

−0.19**

−0.27**

−0.04

 

(0.09)

(0.12)

(0.12)

Age at arrival

−0.01*

−0.01

−0.02***

 

(0.01)

(0.01)

(0.01)

Duration of stay

0.05***

0.02

0.09***

 

(0.02)

(0.02)

(0.02)

Unemployed

−0.19**

−0.41***

−0.10

 

(0.09)

(0.12)

(0.12)

Less education

−0.40***

−0.19

−0.34*

 

(0.12)

(0.15)

(0.18)

No Swiss language skills

−0.33***

−0.59***

0.14

 

(0.13)

(0.17)

(0.16)

Low-educ. * No-language

0.29*

0.42*

−0.31

 

(0.18)

(0.22)

(0.24)

No private network

−0.14

−0.08

−0.09

 

(0.10)

(0.13)

(0.13)

No professional network

0.24**

0.25*

−0.09

 

(0.10)

(0.13)

(0.13)

Improved economic situation

−0.11***

−0.10**

−0.07

 

(0.04)

(0.05)

(0.05)

Qualification match

0.08

0.03

0.14

 

(0.11)

(0.13)

(0.15)

Assembly representation

1.25*

1.27

3.41***

 

(0.68)

(0.88)

(1.08)

Migration difficulties

0.21***

0.15***

0.15***

 

(0.03)

(0.03)

(0.03)

Satisfaction about migration

−0.13***

−0.14***

−0.09***

 

(0.03)

(0.03)

(0.03)

Attachment to Switzerland

−0.14***

−0.09**

−0.18***

 

(0.03)

(0.04)

(0.04)

Origin: Europe

ref.

ref.

ref.

Origin: N-America

−0.28**

−0.59***

−0.11

 

(0.13)

(0.2)

(0.16)

Origin: S-America

0.15

−0.35**

0.75***

 

(0.12)

(0.18)

(0.16)

Origin: Asia

0.20*

−0.52***

0.28*

 

(0.12)

(0.17)

(0.15)

Origin: W-Africa

0.78***

−0.46

1.48***

 

(0.30)

(0.46)

(0.33)

Constant

0.25

−0.61

−3.12***

 

(0.74)

(0.95)

(1.16)

Observations

5189

5189

5189

Log Likelihood

−2989

−2169

−1912

Akaike Inf. Crit.

6019

4378

3864

Notes: *p < 0.1; **p < 0.05; ***p < 0.01; logit coefficients, standard errors in parentheses. Outcome variables: experience of discrimination, experience of discrimination at work, experience of discrimination in public; shown are the log odds with predicted probabilities provided in the text. See Table 9.5 in the Appendix for additional models

Source: Migration-Mobility Survey 2016. Weighted results

Table 9.4

Question wording

Label

Type

Question wording

discrimination (general)

binary yes/no

“Have you experienced situations of prejudice or discrimination in Switzerland in the last 24 months?”

discrimination (work)

binary yes/no

“Where did you experience this discrimination? Was it during education and work?”

discrimination (public)

binary yes/no

“Where did you experience this discrimination? Was it in shops, in public and/or during leisure activities?”

no local language skills

binary if ≠1 (fluently)

“How well do you speak the local language?” [1 (best) – 5]

born outside the EU

binary if ≠EU

“In which country were you born?”

lack of private network

binary if both answered with “no”

“From whom did you receive support? Relatives in Switzerland” and “... Friends in Switzerland”

lack of prof. network

binary if both answered with “no”

“From whom did you receive support? Business relationships/colleagues in Switzerland” and “... Your employer”

improved economic situation

ordinal scale 1–5

“Concerning your professional situation, what would you say overall when comparing your situation today with your situation before moving to Switzerland? It has …” [1 (worsened substantially – 5 (improved substantially)]

qualification match

binary if all answered with “no”

“What are the reasons for you currently occupying a job that does not correspond to your educational level? Inadequate knowledge of one of the national languages” and “... Qualifications obtained abroad are not recognized in Switzerland” and “... To avoid unemployment” and “... Origin, religion or social background”

exposure to diversity

continuous

Representation of ethnic minority groups in the national assembly (Ruedin 2009)

migration difficulties

ordinal scale 1–7

mean of: “On a scale from 0 (not problematic at all) to 7 (very problematic), how problematic were the following aspects when moving to Switzerland? Dealing with the administration (e.g., permits)” and “... Speaking/understanding the local language”

satisfaction about migration

ordinal scale 1–10

“On a scale from 0 (not at all satisfied) to 10 (completely satisfied) can you indicate your degree of satisfaction for each of the following points? With your decision to move to Switzerland”

attachment to CH

ordinal scale 1–10

“On a scale from 0 (no feeling of attachment) to 7 (strong feeling of attachment), to what extent do you have a feeling of attachment to Switzerland?”

Source: Migration-Mobility Survey 2016. Questionnaire

Table 9.5

Additional models

 

Discrimination (general)

Discrimination (work)

Discrimination (public)

(1)

(2)

(3)

(4)

(5)

(6)

Female

−0.05

−0.16*

−0.13

−0.25**

0.06

−0.02

 

(0.08)

(0.09)

(0.11)

(0.11)

(0.11)

(0.12)

Age at arrival

−0.01***

−0.01**

−0.01

−0.01

−0.02***

−0.02***

 

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

Unemployed

0.03*

0.04***

0.01

0.01

0.07***

0.08***

 

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

Duration of stay

−0.12

−0.18*

−0.36***

−0.40***

0.00

−0.09

 

(0.08)

(0.09)

(0.11)

(0.12)

(0.11)

(0.12)

Less education

−0.31***

−0.40***

−0.12

−0.19

−0.33**

−0.37**

 

(0.11)

(0.12)

(0.14)

(0.15)

(0.16)

(0.17)

No Swiss language skills

−0.04

−0.25**

−0.39**

−0.56***

0.43***

0.24

 

(0.11)

(0.12)

(0.16)

(0.17)

(0.15)

(0.16)

Less educ. * No language skills

0.29*

0.26

0.42**

0.40*

−0.21

−0.30

 

(0.16)

(0.17)

(0.21)

(0.22)

(0.22)

(0.24)

No private network

−0.07

−0.12

−0.05

−0.06

0.02

−0.07

 

(0.09)

(0.1)

(0.12)

(0.13)

(0.12)

(0.13)

No professional network

0.32***

0.27***

0.32***

0.28**

0.01

−0.04

 

(0.09)

(0.10)

(0.12)

(0.13)

(0.12)

(0.13)

Improved economic situation

 

−0.21***

 

−0.21***

 

−0.17***

  

(0.04)

 

(0.05)

 

(0.05)

Qualification match

 

0.04

 

−0.01

 

0.09

  

(0.10)

 

(0.12)

 

(0.14)

Assembly representation

 

1.38**

 

1.39

 

3.63***

  

(0.67)

 

(0.88)

 

(1.11)

Migration difficulties

 

0.24***

 

0.20***

 

0.19***

  

(0.02)

 

(0.03)

 

(0.03)

Origin: Europe

ref.

ref.

ref.

ref.

ref.

ref.

Origin: N-America

−0.33***

−0.52***

−0.56***

−0.75***

−0.32**

−0.39**

 

(0.12)

(0.12)

(0.19)

(0.19)

(0.15)

(0.15)

Origin: S-America

0.25**

0.03

−0.31*

−0.46***

0.50***

0.62***

 

(0.11)

(0.12)

(0.16)

(0.17)

(0.13)

(0.16)

Origin: Asia

0.26**

−0.03

−0.39***

−0.72***

0.29**

0.04

 

(0.11)

(0.11)

(0.15)

(0.16)

(0.14)

(0.14)

Origin: W-Africa

0.67***

0.73**

0.19

−0.51

0.91***

1.35***

 

(0.11)

(0.31)

(0.14)

(0.46)

(0.13)

(0.33)

Constant

−0.25

−1.14

−1.07***

−1.83*

−1.39***

−4.39***

 

(0.22)

(0.72)

(0.28)

(0.94)

(0.31)

(1.18)

Observations

5973

5210

5973

5210

5973

5210

Log Likelihood

−3428

−3093

−2426

−2235

−2145

−1982

Akaike Inf. Crit.

6885

6222

4880

4507

4319

4000

Notes: *p < 0.1; **p < 0.05; ***p < 0.01; logit coefficients, standard errors in parentheses. Outcome variables: experience of discrimination, experience of discrimination at work, experience of discrimination in public; shown are the log odds

Source: Migration-Mobility Survey 2016. Weighted results

With the exception of origin, the drivers of experiencing discrimination are only weak predictors. Variables such as gender, less education, or a lack of language proficiency are typical drivers of ‘observable’ discrimination; that is, they explain a substantial part of differences in labour market outcomes in terms of wages or unemployment duration (e.g., Auer et al. 2017; Koopmans 2016) and in other markets such as housing (e.g., Carlsson and Eriksson 2014). The results show that most of these variables have limited influence on individual perception of discrimination. For instance, immigrants who have been in Switzerland for a longer period are more likely to report discrimination. The predicted probability of experiencing discrimination of a person who stayed for 2 years is 49%, compared with 59% for an otherwise equivalent person with a stay of 10 years.5 These results could be due to an increasing likelihood of experiencing an incident of discrimination with duration of stay. At the same time, it is possible that the effect of acceptance (that is, an improvement in a person’s economic situation as discussed below) diminishes over time and less recent immigrants start to notice differential treatment and perceive it as unfair. In contrast, whereas a lack of language proficiency is associated with a higher propensity to report discrimination, the substantive difference is limited (predicted probabilities of 53 and 45%). It is possible that this result is biased by highly skilled immigrants working in international businesses and organizations, in which it is often more common to communicate in English rather than in one of the Swiss languages. Moreover, it is possible that a complete lack of language skills constrains the capability to notice discrimination, particularly in the spheres of social mistreatment. If we interact language ability with level of education, the sign of the coefficient suggests that less-educated immigrants without local language skills might indeed be more likely to report discrimination, although the standard errors around the estimate are rather large, particularly for discrimination in public. Although less-educated immigrants might be concentrated in economic sectors with many immigrants – perhaps objectively preferred in hiring (Auer et al. 2018) – these immigrants can however experience discrimination during their workday or outside work. Moreover, immigrants who report lacking a supportive professional network are substantially more likely to report discrimination in general (predicted probabilities of 53 and 59%). Intuitively, being unemployed only affects discrimination in general and in public situations, but not at work. The negative coefficient could be explained by a lower general activity of the unemployed (first highlighted in the well-known study by Jahoda and Zeisel 1933), that is, the potential number of situations in which discrimination can occur diminishes when people are unemployed (predicted probabilities of 48% for unemployed people compared with 53% otherwise).

The apparent key determinant among the drivers is the immigrant’s origin. We observe a constant increase in the propensity to report general discrimination when moving from immigrants originating from European countries (predicted probability of 53%) to North-American (57%), South-American (58%), and West-African countries (71%). This finding is in line with a large corpus of literature on the graded effects due to so-called ethnic rankings (Hagendoorn 1995; Zschirnt and Ruedin 2016). In contrast, the predicted probability of reporting discrimination for immigrants from Asian countries is less than what can be expected from ethnic hierarchies (46%), and we expect this probability to reflect different response behaviour with respect to the outcome variable.

Interestingly, the statistical effect of origin differs between reported discrimination in the workplace and in private situations. Although compared with European immigrants, immigrants from the Americas are significantly less likely to report discrimination at work, they are more likely to report discrimination in public situations; in general, the results are the opposite for immigrants from ‘more distant’ African countries. This result might capture effects of direct competition in the labour market versus working in occupations “reserved” for immigrants from culturally more distant countries.

In general, we find that individuals distinguish, to a surprising degree, between discrimination within and outside the work environment. For instance, respondents often report discrimination in the workplace but not in a public environment when they are less educated and lack local language skills. In contrast, age and duration of stay affect discrimination reporting in the public rather than the work sphere. Improvements in employment relative to the pre-migration situation reduce perceived discrimination in the workplace, whereas minority representation in the country of origin affects discrimination in the public context but not at work.

The reported experience during migration and attachment appear decisive with respect to reporting discrimination. The items of the attachment category are strong predictors of perceived discrimination. Both a currently higher level of attachment to the Swiss society (that is, “a certain feeling of attachment to Switzerland” – predicted probabilities are 69 and 35% for the minimum and maximum level of attachment observed, respectively) and positive past experiences during the migration process (that is, “no difficulties during the migration process” – predicted probabilities are 39 and 73% for the minimum and maximum level of attachment observed, respectively; for “overall satisfaction with the decision to migrate to Switzerland”, predicted probabilities are 48 and 76% for the minimum and maximum level of attachment observed, respectively) significantly reduce the propensity to report incidents of unfair treatment. In Table 9.5 we include additional models to ascertain whether the reported statistical effects are robust against model specifications.

9.6 Discussion and Conclusion

In this chapter, we have examined who among recent immigrants to Switzerland is more likely to report discrimination in a survey when asked. After controlling for “observable” drivers, discrimination should occur, on average, with the same probability across individuals. However, immigrants have had different “unobservable” experiences and come with different socialization and notions related to their migration that are likely to affect their perception of discrimination – presumably beyond its actual occurrence. The findings in this chapter indicate that such elements of acceptance and attachment influence the perception of differential treatment as discriminatory in an essential way. We thereby also highlight the interdependency between different aspects of the Migration-Mobility Nexus, particularly that the exclusionary logic of the societal sphere is substantially influenced by the inclusionary logic of the economic sphere and that robust findings of discrimination along the Swiss dual migration and mobility regime at the aggregate level (i.e., EU/EFTA vs. non-EU/EFTA nationals) can be blurred by variation in individual perceptions and feelings of attachment to the host society. Although improvements in a person’s economic situation – we refer to a higher acceptance – mitigate perceived discrimination, issues during the migration process or a lack of attachment – that is, by immigrants we assume to “anticipate” further problems ahead – are associated with substantially higher levels of perceived discrimination. This result indicates that studies capturing objective levels of discrimination, such as field experiments on hiring (Zschirnt and Ruedin 2016), can be inadequate to capture perceptions of discrimination relevant to well-being.

Despite the usual limitations and calls for cautious interpretation that we elaborate below, the findings are highly plausible and entail at least two main implications for research and for policymaking. First, policies focussing on “objective” discrimination might fail to address social cohesion – and, indirectly, the politicization of immigrants (Van der Brug et al. 2015). If discrimination is a blind spot in the perception of both policymakers and victims (and not least offenders), not only legal frameworks to mitigate unfair treatment but also other assessments such as surveys fail to capture the true extent of discriminatory behaviour and might ultimately be ineffective.

Second, research should generally focus more on perceptions of discrimination. From a purely empirical perspective, we can conclude that contemporary individual-level measures will most likely fail to capture discrimination that goes beyond differences in wages for individuals with equal skills, for instance. In other words, analyses such as residual studies might provide us with valuable insights about observable disadvantages for observable individual traits, but we do not learn much about whether disadvantaged individuals perceive their lower wages as due to discrimination in society, to stay with the example. If they do, we must follow up and investigate why they fail to change their situation and what could be done from a policy perspective. If they do not, we are left wondering what led to a situation in which inequality that is not grounded on objective criteria is not perceived as unfair. Both aspects must be properly investigated and understood.

In this respect, one limitation of this study is also one of its key findings. It is safe to assume that reporting of discrimination is biased; who reports being discriminated against in a survey is a function of not only actual discrimination but also a complex array of individual characteristics. In this analysis, we tried to enumerate this selection bias. If we consider policy implications, establishing “objective” discrimination is not sufficient because from the perspective of the victims, what matters is the perception. In other words, we can imagine a world in which many are discriminated against according to our “objective” criteria but who do not feel that way (and the opposite – no objective discrimination, but feelings of discrimination). If individual well-being and social cohesion are the criteria of interest, we must take perceptions seriously.

Considering selection bias, we must assume that perception of discrimination correlates with reporting of discrimination. Although the assumption is a common one (because it is not verifiable) in all surveys – not only those on discrimination – the correlation might be blurred by increasing sensitivity to the question asked (Rosenfeld et al. 2015). This point is particularly true in situations in which there might be consequences for the respondent, such as when admitting to an illegal activity (which the respondent can indeed perceive as illegal and unjust), or when reporting minority status in a context in which violence and discrimination can be expected (Ruedin 2018a). Reporting discrimination in a Western country such as Switzerland most likely constitutes a somewhat sensitive topic but cannot be compared with admitting illegal activities. We argue that by considering key elements that drive individual perception, we also capture reporting behaviour to the same extent. In particular, we interpret the plausible differences between determinants of discrimination at work and in public situations as an indication that this assumption has merit.

In summary, we demonstrate how individual characteristics and experiences influence the perception of discrimination. Although a broad corpus of literature has shown that discrimination against immigrants is a widespread phenomenon (Zschirnt and Ruedin 2016 and Zschirnt, E. (2018, March 23). Ethnic discrimination in the Swiss (German) labour market – first results from a correspondence test. Presented at the NCCR Research Day #5, Neuchâtel), individual perceptions might deviate from this observation. It is therefore important to consider this additional dimension for both policymakers who seek to raise awareness of this matter and researchers who infer the occurrence of discrimination from observational data such as surveys or interviews. We conclude that contemporary individual-level measures and policy recommendations merely approximate discriminatory patterns and urge future research to consider factors that affect individual perception of discrimination.

Footnotes

  1. 1.

    When nationality is taken as the criterion – the data that are readily available over time – the growth in the immigrant population post-1970s correlates strongly with its diversity. Using the Herfindahl index to express the diversity of the population, we observe an increase from 0.07 in the mid-1970s to 0.25 in the early 2010s. After the early 2010s, the Herfindahl index stopped increasing as much as it did previously.

  2. 2.

    These questions can entail individually experienced discrimination and situations in which someone in the vicinity of the respondent was discriminated against. Although such experiences could bias the effect of individual-level drivers of discrimination such as education, they should not affect individual perception of discrimination per se.

  3. 3.

    We refrain from defining a specific pathway because both networks as a decreasing factor for discrimination and discrimination as a decreasing factor for networks are possible.

  4. 4.

    Table 9.5 in the Appendix presents partial models.

  5. 5.

    To calculate predicted probabilities, we set all binary variables to 0, all continuous variables to their mean and the region to neighbouring countries. We use discrimination in general as the outcome variable.

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Authors and Affiliations

  1. 1.WZB Berlin Social Science CenterBerlinGermany
  2. 2.nccr – on the moveNeuchâtelSwitzerland
  3. 3.University of NeuchâtelNeuchâtelSwitzerland
  4. 4.University of the WitwatersrandJohannesburgSouth Africa

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