Life Satisfaction Among Ethnic Minorities in Europe

Abstract

Although life satisfaction (LS) of ethnic minorities can be significantly undermined by several psychological and material hardships, relatively few studies have examined this issue across Europe. The aim of the present study is to examine the impact of belonging to an ethnic minority group on LS, using data from the sixth wave of the European Social Survey (ESS6), carried out in 2012 in 29 countries (N = 54,540). About 6.7% of all the participants in the ESS6 considered themselves belonging to an ethnic minority group. Our findings show that LS is lower for the ethnic minorities than for the majority not only in the pooled ESS6 sample but also in 19 European countries, most notably in Slovakia and in the Czech Republic. Multilevel analysis indicated that the negative impact of ethnic minority status on LS tended to be enhanced in ex-Communist countries as well as in countries with higher ethnic diversity. The findings of this study show clearly that policy makers of several European countries should focus on increasing social justice and solidarity, and providing ethnic minorities real opportunities to feel more integrated into society.

Keywords

subjective well-being life satisfaction ethnicity ethnic minorities cross-cultural comparison

Most European nations are now multiethnic, and, given current demographic trends, there is reason to believe that societies will continue to become more ethnically and culturally diverse (Sheikh, 2006). There are approximately 60 million ethnic and religious minorities in the European Union, making up about 12% of Europe’s total population (Privot & Demirovski, 2014). Compared with the socioeconomic consequences of migration, the psychological impact of belonging to ethnic minority groups has been considerably less studied (Missinne & Bracke, 2012). However, it is known that disadvantaged and/or minority social groups face a number of threats to their psychological well-being. Earlier research on ethnic minorities’ psychological state has explored not only mental disorders (Mangalore & Knapp, 2012), such as depressive symptoms (Missinne & Bracke, 2012) and psychosis (Kirbride et al., 2008), but also health-related quality of life (Luckett et al., 2011), life satisfaction (LS; Kirmanoglu & Baslevent, 2014), and psychological well-being (Heim, Hunter, & Jones, 2011; Molix & Bettencourt, 2010; Verkuyten & Lay, 1998). The current study focuses on examining LS among ethnic minorities in 29 European countries.

Broadly speaking, LS refers to how a person evaluates his or her life in general. Alongside pleasant and unpleasant affect (also called the affective component of subjective well-being [SWB]) general LS is one of the three key components of SWB (Diener, Suh, Lucas, & Smith, 1999). People have high levels of SWB when they feel many positive and few negative emotions, and when they are satisfied with their life (Diener, 2000). However, previous research has also shown that people from different cultures assess positive or negative emotions differently in determining how satisfied they are with their lives (Kööts-Ausmees, Realo, & Allik, 2013; Kuppens, Realo, & Diener, 2008) and that the strength of the relationship between LS and affective components of SWB depends on many different factors, such as cultural values (Kuppens et al., 2008; Suh, Diener, Oishi, & Triandis, 1998) and personality traits (Kööts-Ausmees et al., 2013), among others.

LS has a long-term component due to relatively stable factors (e.g., personality and genetic factors; Steel, Schmidt, & Shultz, 2008; Weiss, Bates, & Luciano, 2008), a moderate-term component due to recent life events (e.g., unemployment, work load, or perceived health; Lucas, Clark, Georgellis, & Diener, 2004; Røysamb, Tambs, Reichborn-Kjennerud, Neale, & Harris, 2003), and a short-term state component (e.g., due to current mood; see Pavot & Diener, 1993, for discussion). Thus, although LS is relatively stable over time (Lucas & Donnellan, 2007), certain life events and circumstances—such as marriage, divorce, unemployment, bereavement, and migration, for instance—can have a significant impact on LS (Luhmann, Hofmann, Eid, & Lucas, 2012).

From previous research, which is described below, it seems that ethnic minorities tend to experience some stressful life events (e.g., unemployment, discrimination) more frequently than the native population. This raises the question, whether ethnic minority status is one of those relevant factors that could influence the levels of LS. Consequently, the main goal of the current study is to examine LS among ethnic minorities in comparison with ethnic majorities across Europe. There is surprisingly little cross-national research on this topic as most studies have limited their analyses with a few countries or ethnic groups (e.g., Schaafsma, 2011; Verkuyten, 2008).

Individual-Level Factors Related to the Welfare of Ethnic Minorities

For many ethnic minority groups, everyday life in the country of settlement raises questions of belonging, acceptance, and of psychological as well as sociocultural adaptation (Verkuyten, 2008). If ethnic minorities are less satisfied with their lives compared with the ethnic majority, there could be a variety of reasons behind this negative tendency—from perceived discrimination to a lack of social support—and all these factors need to be taken into account to understand the real cross-national variance in ethnic minorities’ LS.

Perceived Discrimination

Ethnic minorities are often devalued by the larger society, and therefore, members of these groups experience stigmatization and discrimination (cf. Molix & Bettencourt, 2010). Ethnic minorities report negative treatment across wide variety of life domains, including education, employment, income, and health (Outten, Schmitt, Garcia, & Branscombe, 2009; Verkuyten, 2008). For instance, a recent large-scale study in Switzerland showed that different immigrant groups were more likely than locals to report that they had been discriminated against, based on their ethnicity or nationality, in their workplace during the last year (Krings, Johnston, Binggeli, & Maggiori, 2014). Discrimination has been suggested to be a possible explanation for increased incidence of mental health problems among ethnic minorities (Veling, Hoek, & Mackenbach, 2008). Discrimination is also a relevant factor in employment options, for example, in the form of multiple barriers that ethnic minorities face while entering the labor market (Leping & Toomet, 2008). Thus, discrimination can take place at different levels—from interpersonal experiences to structural discrimination by institutions—but regardless of its form, it can adversely affect individuals’ well-being (cf. Veling et al., 2008). Kirmanoglu and Baslevent (2014) analyzed cross-national data from the European Social Survey (ESS) and found that the magnitude of the negative impact of discrimination perceptions depends on the type of discrimination as well as minority membership status. Taken together, minority members who feel discriminated against are likely to be less satisfied with their life in the country of settlement (Kirmanoglu & Baslevent, 2014; Verkuyten, 2008).

Unemployment

As said above, unemployment has been associated with both short- and long-term declines in LS (Lucas et al., 2004; Wulfgramm, 2014; Yap, Anusic, & Lucas, 2012). Bearing this in mind, it is important to note that in general, ethnic minorities have lower chances of getting a job (Bevelander & Veenman, 2004) than members of a majority ethnic group. However, employment patterns vary across the diverse set of ethnic minorities because of occupational clustering. As an example, in the United Kingdom, the Indian Hindus are over-represented among health professionals, and therefore they earn even more on average than White British Christians whereas all the other groups earn less (Longhi, Nicoletti, & Platt, 2013). And, it is also possible that members of certain ethnic minority groups (such as French and German immigrants in Switzerland, for instance) have even higher employment rates than locals (Krings et al., 2014), but this is more an exception than the rule. Another source of variation in ethnic minorities’ labor market success is exposure to the native population: According to the Swedish data, for instance, having a native partner is strongly related to greater chances of getting a job (Tammaru, Strömgren, Stjernström, & Lindgren, 2010). The magnitude of ethnic disparities in employment can also be influenced by sociopolitical change. For example, after the collapse of the Soviet Union in early 1990s, when Estonia was building up political and economic institutions of the new nation-state, mostly Estonian workers (i.e., individuals from Estonia’s majority ethnic group) were hired (Leping & Toomet, 2008). Altogether, minorities’ employment situation should not be neglected, because in addition to being a source of income and returns from the social security system, it also facilitates social contact, adds to identity formation, and structures time for those who work (Bevelander & Veenman, 2004).

Level of Education

Ethnic minorities’ labor market success is closely related to their education level. For instance, data show that educated ethnic minorities have a better chance of finding a job in the Dutch job market than their peers with lower education—but they are still at a disadvantage against people with two Dutch parents (Van Jaarsveldt, 2015). Education itself also seems to have a positive impact on many factors related to well-being (e.g., health), but the results from different studies have been mixed. Data from the British Household Panel Survey have shown that education is positively associated with LS, but only for the least happy individuals (Binder & Coad, 2011). From a different angle, Yakovlev and Leguizamon (2012) have reported that higher education (college degree) has a relatively strong positive effect on SWB, but secondary education (high school) does not. In addition to being relevant to LS judgments, education is also one of the factors that tends to divide ethnic majority and minority groups. The educational performance gaps between ethnic minorities and the majority are believed to be largely explained by socioeconomic differences and language barriers (Froy & Pyne, 2011). Language difficulties were underlined by a large-scale study in the United Kingdom, which showed that at the beginning of school, pupils from ethnic groups substantially lag behind native born pupils, but this gap declines throughout compulsory schooling (Dustmann, Machin, & Schönberg, 2010). Other contributing factors to achievement gap between majority and minority populations include ethnic minority members’ family background and parental expectations, financial pressures, low aspirations, and a lack of awareness of education options (Froy & Pyne, 2011). In addition, teachers and school leaders often do not feel qualified or sufficiently supported to teach students with multicultural, bilingual, and diverse learning needs (Organisation for Economic Co-Operation and Development [OECD], 2010). Several studies have also shown that there are significant ethnic differences in the United Kingdom in the incidence of over-education (e.g., Battu & Sloane, 2002, 2004; Rafferty, 2012)—minority groups (especially Black Africans and Pakistanis/Bangladeshis) are more likely to be over-educated, and as a result, also more disadvantaged in the labor market than their White U.K.-born counterparts (Rafferty & Dale, 2008).

Health Status

Numerous studies have shown that SWB and its components are associated with better self-reported health (see Friedman & Kern, 2014, for a review), and SWB even contributes to longevity (Diener & Chan, 2011). In their systematic review, Nielsen and Krasnik (2010) concluded that in regard to self-perceived health status, most ethnic minority groups appeared to be disadvantaged as compared with the majority population. However, minorities’ poorer health status does not only appear in self-reports. For example, recent health surveys have demonstrated higher prevalence of obesity issues among racial/ethnic minorities (Crossrow & Falkner, 2004) as compared with ethnic majorities. Overweight and obesity are, in turn, strong risk factors for many different types of cancers (Doyle, 2010), type 2 diabetes (Crossrow & Falkner, 2004), and cardiovascular disease (Tran et al., 2011). Economic disadvantage (Mangalore & Knapp, 2012), poverty’s adverse effects on mental health (Price, Khubchandani, McKinney, & Braun, 2013), lifestyle behaviors (Baburin, Lai, & Leinsalu, 2011), and access to, and quality of, health care (Price et al., 2013; Scheppers, van Dongen, Dekker, Geertzen, & Dekker, 2006) are believed to account for some of the ethnic disparity in health status and disease prevalence.

Social Support

Research has also established that one of the most consistent predictors of SWB is the quality of social relationships (Diener & Seligman, 2002), and that the key in understanding their association probably lies in positive social connections (Kok et al., 2013) and in perceived social support (Siedlecki, Salthouse, Oishi, & Jeswani, 2014). Greater positive social support from friends and family and stronger social relationships are related not only to greater LS but also to lower rates of depression, lower anxiety, and higher self-esteem (cf. Outten et al., 2009). Social support can buffer individuals from the harmful effects of stressful situations and reduce mortality risk (Holt-Lunstad, Smith, & Layton, 2010; Sundberg, 2001). However, ethnic minorities, who often happen to be individuals immigrating to another country, cannot always rely on the benefits of supporting social networks—social isolation and poor social networks have shown to be important psychosocial factors in ethnic minorities’ suicide (Ferrada-Noli, Asberg, Ormstad, & Nordström, 1995). Yet, there are also studies showing that disadvantaged ethnic minority groups may develop strong support networks among their co-ethnics and extended family members to cope with discrimination and material hardship (Almeida, Molnar, Kawachi, & Subramanian, 2009; House, Umberson, & Landis, 1988).

The Impact of Country-Level Contextual Factors

Europe is characterized by a mixture of cultures, languages, economies, traditions, and religions, and each country also consists of diverse populations in terms of ethnic, cultural, and religious background. Thus, the welfare of ethnic minorities throughout Europe is likely not uniform. Ethnic minority groups might cope better and have higher levels of LS in some European countries but not in others, due to some country-level contextual factors that buffer against the negative effects of belonging to an ethnic minority group. Three important areas of country-level differences considered in this study are countries’ overall development level, the legacy of Communism, and countries’ degree of ethnic diversity.

Human Development Level

High human development means living a long and healthy life, being educated, and having a decent standard of living; and, this is both augmented and facilitated by political freedom, guaranteed human rights, and personal self-respect (Kelley, 1991). On one hand, ethnic minorities may be more satisfied with their lives in successful and prosperous environments because the social system benefits and services of highly developed countries might meet the needs of minority ethnic groups more effectively than those in less developed countries. On the other hand, the well-off surroundings could make the socioeconomic disparities of ethnic minorities and the majority even more contrast, because in rather wealthy countries individuals might still have unequal opportunities to engage in broad-ranging societal participation. For example, the highly developed countries Greece, Ireland, and Italy have scored lower than the European Union average on the social justice index, which relates to poverty prevention, equitable education, labor market access, social cohesion and non-discrimination, health, and intergenerational justice (Schraad-Tischler & Kroll, 2014).

The Legacy of Communism

In relation to general human development level, it should also be taken into account that radical social, political, and economic changes took place in a considerable number of Eastern European countries in 1990s. Despite remarkable economic and social developments in many former so-called Soviet-bloc countries, they still show lower levels of LS than their economic levels would predict. Thus, the legacy of Communist rule—although it ended nearly 25 years ago—is still used as the best explanation for low levels of LS of people in the so-called ex-Communist countries (Inglehart, Foa, Peterson, & Welzel, 2008). In several of those countries, the relatively large Russian-speaking population (which mainly had arrived in the country during the Soviet period) found themselves in a new minority status after the countries’ independence was restored, which created insecurity and perceived threat to their ethnic identity (Ehala, 2009). Together with diminishing opportunities of social participation and diminished labor market prospects (Kus, 2014), this could lead to ethnic minorities’ feelings of frustration and dissatisfaction in the ex-Communist countries. Thus, it is important to analyze European ethnic minorities’ LS in the context of recent historical and political background.

Ethnic Diversity

Ethnic heterogeneity or diversity of a country can have benefits as well as drawbacks in terms of well-being. On one hand, it has been argued that greater ethnic diversity could lead to greater possibility of misunderstandings or conflicts among people of different cultures and a decrease in social capital (Longhi, 2014; Putnam, 2007). On the other hand, ethnic minorities in an ethnically heterogeneous country might have greater chances to develop supporting social relationships with individuals with similar experiences. A rather robust country-level factor that has been thought to be related to countries’ heterogeneity is its population size. According to Fischer’s (1975) subcultural theory of urbanism, large population size should increase the likelihood that any individual (no matter how unusual his or her characteristics) will be able to find others to whom he or she is similar. When the number of individuals considering themselves belonging into the same group grows larger (in relation to larger population size), it may positively affect minorities’ identity construction and maintenance (Cornell & Hartmann, 2007). Individuals who identify more strongly with their heritage group have been shown to report greater SWB (Ghavami, Fingerhut, Peplau, Grant, & Wittig, 2011; Schaafsma, 2011) and lower levels of perceived stress (Heim et al., 2011). In line with Tajfel’s (1978) social identity theory, group identification implies a sense of belonging that might attenuate or buffer the negative effects of perceived discrimination on LS (Verkuyten, 2008). However, it also matters how heterogeneous the country’s ethnic minority population itself is. If a country’s ethnic minority population mainly consists of one large ethnic group, there are probably more opportunities for intra-group social relationships and cultural activities; but, if a country’s ethnic minority population consists of many different and small ethnic groups, it could result in a fractionalized community with diminished social capital (Longhi, 2014; Putnam, 2007) and lower levels of well-being.

The Present Study

The aim of the current study is to examine the level of LS among the members of ethnic majority and minority groups across 29 European countries. We are interested in LS of individuals who consider themselves as being part of an ethnic minority group, regardless of their country of origin, citizenship, immigration history, and so forth, in comparison with those individuals who identify themselves as members of a majority group. The term ethnic minority is not equivalent to immigrant, because the former might also refer to indigenous ethnic minorities. Within each country, the ethnic minority population is clearly diverse, but the present study explores the general psychological effect of perceiving oneself as part of an ethnic minority group. Because minorities may face different negative factors on daily basis, we expect their LS levels to be lower than those of the majority population, but at the same time, the strength of the relationship between LS and ethnic minority status is expected to vary across countries. More specifically, we believe that certain contextual factors, such as countries’ development level, legacy of Communism, and overall ethnic diversity, moderate this relationship. But before proceeding to the analysis of country-level moderators of the association between LS and ethnic minority status, we also take into account several factors that likely influence LS judgments at the level of individuals—perceived discrimination, social support, health, and unemployment status, as well as age, gender, and education level. Namely, when there indeed are significant LS differences between the ethnic minority and majority groups, it is important to know whether these differences could be explained by individual differences in common sociodemographic and psychosocial variables, or whether the differences between ethnic minority and majority groups in European countries remain significant even when the individual-level factors are being accounted for.

To the best of our knowledge, no previous study has systematically analyzed LS judgments of ethnic minority and majority groups using the nationally representative data from the ESS, while taking into account the relevant individual- as well as cultural-level indicators in the context of the hierarchical data structure. Although Kirmanoglu and Baslevent (2014) have also used the ESS to examine LS among European ethnic minorities, they specifically focused on the individual-level interactions between immigration and discrimination. Yet, when individuals are nested within countries, both levels of data should be analyzed simultaneously via multilevel models (Raudenbush & Bryk, 2002), focusing on the individual (i.e., disaggregated) as well as the group (i.e., aggregated) levels of the structure at the same time (Byrne et al., 2009). Therefore, in the present study we will employ the technique of multilevel random coefficient modeling (MRCM), which is considered to provide the most accurate analyses of multilevel data structures (Nezlek, 2001; Raudenbush & Bryk, 2002). And, unlike Kirmanoglu and Baslevent (2014) who used data from the fifth round of the ESS, we examine the most recent wave (Round 6, 2012), which has the advantage of covering more European countries compared with the previous round. Thus, our results could be generalized to a larger population of European ethnic minorities.

Method

Sample

The analyses are based on the sixth wave of the biennial multicountry survey, the ESS (European Social Survey Round 6 Data, n.d.; http://www.europeansocialsurvey.org/), conducted in 2012. We used data for 29 countries: Albania, Belgium, Bulgaria, Czech Republic, Cyprus, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Kosovo, Lithuania, the Netherlands, Norway, Poland, Portugal, the Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom, and the Ukraine. Sample sizes in each country ranged from 752 (in Iceland) to 2,951 (in Germany). Across the 29 countries, there were 54,540 participants in the ESS Round 6 (ESS6; for sample characteristics, please see Table 1). The survey involves strict random probability sampling and rigorous translation protocols. The average response rate was approximately 62%, ranging from 34% in Germany to 79% in Albania (retrieved from http://www.europeansocialsurvey.org/data/deviations_6.html). The overall mean age of participants was 48 years (SD = 19 years), and approximately 54% of all participants were females.

Table 1.

Sample Characteristics of the 29 Countries in the Sixth Round of the ESS.

Country	N	M age (SD)	% women (n)	% ethnic minority (n)	% EM in country’s population^a
Albania	1,183	43.77 (18.46)	54.29 (652)	4.58 (55)	17.4
Belgium	1,869	47.33 (19.08)	51.26 (958)	5.62 (105)	9.1
Bulgaria	2,260	54.03 (16.97)	57.61 (1,302)	17.79 (402)	15.2
Czech Republic	1,981	47.76 (17.15)	50.62 (1,017)	2.24 (45)	36.3
Cyprus	1,113	47.56 (18.49)	55.91 (624)	3.67 (41)	16.0
Denmark	1,650	48.70 (19.02)	49.46 (816)	3.46 (57)	10.4
Estonia	2,380	49.41 (19.51)	57.98 (1,380)	19.83 (472)	31.3
Finland	2,197	49.75 (18.88)	51.12 (1,123)	2.09 (46)	9.6
France	1,968	51.82 (18.48)	55.23 (1,087)	4.52 (89)	10.3
Germany	2,951	48.70 (18.57)	49.59 (1,467)	4.50 (133)	19.3
Hungary	2,014	47.18 (18.18)	55.16 (1,111)	5.81 (117)	1.9
Iceland	752	44.18 (18.69)	49.87 (375)	3.46 (26)	7.0
Ireland	2,625	47.26 (17.91)	52.36 (1,376)	3.39 (89)	12.6
Israel	2,490	44.45 (19.12)	54.31 (1,362)	11.84 (297)	23.6
Italy	939	47.08 (18.28)	51.46 (494)	2.19 (21)	8.0
Kosovo	1,294	43.22 (17.09)	52.28 (677)	12.74 (165)	7.1
Lithuania	2,109	48.45 (18.68)	58.84 (1,241)	6.35 (134)	15.8
Netherlands	1,845	51.17 (18.00)	53.55 (988)	6.56 (121)	20.9
Norway	1,624	46.00 (18.17)	47.17 (766)	6.40 (104)	13.8
Poland	1,898	46.10 (18.85)	52.11 (989)	1.84 (35)	5.4
Portugal	2,151	52.30 (19.01)	60.39 (1,299)	1.63 (35)	5.0
Russia	2,481	46.00 (18.10)	61.72 (1,533)	16.35 (406)	19.1
Slovakia	1,838	49.28 (16.53)	59.34 (1,096)	5.58 (103)	19.3
Slovenia	1,257	48.31 (18.85)	54.10 (680)	2.15 (27)	16.9
Spain	1,888	47.61 (18.01)	51.72 (977)	3.02 (57)	12.0
Sweden	1,845	47.83 (19.01)	48.73 (900)	3.09 (57)	20.3
Switzerland	1,493	47.51 (18.78)	50.03 (747)	8.91 (133)	7.0
Ukraine	2,174	48.51 (18.70)	63.27 (1,378)	4.55 (99)	22.5
The United Kingdom	2,271	51.83 (19.12)	57.39 (1,312)	7.70 (176)	19.5
All countries	54,540	48.31 (18.59)	54.37 (29,727)	6.67 (3,657)	14.9

Note. ESS = European Social Survey; N = number of individuals; EM = ethnic minorities.

Data about countries’ ethnic composition is mainly derived from countries’ national statistics databases, if available online (in most cases the latest census)—for Albania: http://www.instat.gov.al, for Belgium: http://statbel.fgov.be, for Bulgaria: http://www.nsi.bg/census2011/indexen.php, for Czech Republic: http://www.czso.cz, for Cyprus: http://www.cystat.gov.cy, for Denmark: http://www.statbank.dk, for Estonia: http://www.stat.ee, for Finland: http://www.stat.fi, for France: http://www.insee.fr, for Germany: http://www.bpb.de, for Hungary: http://www.ksh.hu/nepszamlalas, for Iceland: http://www.statice.is/, for Ireland: http://www.cso.ie, for Israel: http://www1.cbs.gov.il, for Italy: http://www.demo.istat.it, for Kosovo: http://ask.rks-gov.net/, for Lithuania: http://www.stat.gov.lt/en/, for Netherlands: http://statline.cbs.nl, for Norway: http://www.ssb.no, for Poland: http://www.stat.gov.pl, for Portugal: http://www.ine.pt, for Russia: http://www.perepis-2010.ru, for Slovakia: http://slovak.statistics.sk, for Slovenia: http://www.stat.si, for Spain: http://www.ine.es, for Sweden: http://www.scb.se, for Switzerland: http://www.bfs.admin.ch, for the United Kingdom: http://www.ons.gov.uk, for Ukraine: http://www.ukrstat.gov.ua.

Materials

The ESS is an hour-long face-to-face interview. All individual-level variables and most of the country-level variables described below were taken from the ESS6 database or from the ESS Multilevel Data (n.d.).

Individual-level variables

At the level of individuals, we analyze ratings of LS and self-reported belonging to an ethnic majority or minority group. In addition to taking into account factors that are known to be related to LS ratings (such as subjective health, social support, and employment), we also use demographic variables such as age, gender, and education level as covariates in our analyses.

LS

The overall LS of each participant was assessed by using the following item: “All things considered, how satisfied are you with your life as a whole nowadays?” (Question B20), which was measured on an 11-point Likert-type scale, ranging from 0 (extremely dissatisfied) to 10 (extremely satisfied). The highest overall LS was found to be in Denmark (M = 8.57, SD = 1.50) and the lowest in Bulgaria (M = 4.34, SD = 2.68). Single-item measures of LS have shown to have adequate convergent validity and satisfactory reliability (see Lucas & Donnellan, 2012).

Ethnic minority status

Participants were asked “Do you belong to a minority ethnic group in [country]?” (Question C24). This item was answered dichotomously, either yes (1) or no (2), and we recoded the latter answer so that not belonging to a minority ethnic group received the value 0. Among all participants, 3,657 individuals (i.e., 6.7%) reported that they belonged to an ethnic minority group in their country of residence. The respective share ranged from 1.6% in Portugal to 19.8% in Estonia.

However, comparing the proportion of self-rated ethnic minority membership from ESS6 (Table 1) with national statistics of ethnic composition revealed some discrepancies. Countries’ national statistics data of ethnic composition was drawn from the most recent census (if available electronically). The overall proportion of ethnic minorities was obtained by subtracting the proportion of the main nationality from 100. The correlation between the proportion of self-rated ethnic minority membership per country from ESS6 (Table 1, column 5) and national statistics of ethnic composition (Table 1, column 6) was r = .30 (ns). A closer inspection of the data revealed that ethnic minorities seem to be underrepresented in the ESS6 in all countries except for Switzerland, Bulgaria, Hungary, and Kosovo. Among the remaining 25 countries, the biggest difference by far lies in the Czech Republic where according to the official statistics, 36.3% of the inhabitants are not ethnic Czechs. In the ESS6 data, however, only 2.2% of the respondents in the Czech Republic argued that they belong to an ethnic minority group.¹ If the Czech Republic was excluded from the analyses, the correlation between the two variables (i.e., share of ethnic minorities in ESS6 and in official statistics) was r = .45, p = .016.

Perceived discrimination

The ESS questionnaire measures belongingness to a group being discriminated against by the following question: “Would you describe yourself as being a member of a group that is discriminated against in this country?” (Question C16). The grounds of discrimination is specified by the subsequent question: “On what grounds is your group discriminated against?” (C17), with several option categories. In this study, we examined discrimination on grounds of color/race, nationality, and ethnic group, and combined those into a single variable (the variable became the score 1, if perceived discrimination was reported, and 0 if not). The proportion of individuals who reported belonging to a group discriminated against varied from 0.1% in Poland to 6.8% in Israel, with the mean of 1.6% (SD = 1.6) across all 29 countries.

Self-reported health

The core module of the ESS questionnaire includes an item that measures subjective general health: “How is your health in general?” (Question C7). The item was answered on a 5-point scale, from 5 = very bad to 1 = very good. For this study, the scale was reversed, meaning that higher scores indicated better subjective health. Countries’ means ranged from 3.2 in Ukraine (SD = 0.9) to 4.2 in Ireland (SD = 0.8).

Current unemployment

In one of the rotating modules (Section F) of the ESS, participants were asked about their activities for the past week—among other things, participants also marked whether they had been unemployed and were actively looking for a job (Question F17.03), or unemployed and not looking for a job (Question F17.04). Both items were answered dichotomously (either no [0] or yes [1]), and for the present analyses these were merged into one variable. Altogether 4,779 individuals (8.7%) reported that they had been unemployed in the past week. Countries’ overall current unemployment rates ranged from 2.8% (in Switzerland) to 27.3% (in Albania), with the mean of 9.2% (SD = 5.7) across countries.

Perceived social support

Social support was measured with a single item—“To what extent do you receive help and support from people you are close to when you need it?” (Question D36), which was rated on a scale from 1 (not at all) to 6 (completely). Means ranged from 4.43 (SD = 1.91) in Albania to 5.32 (SD = 0.99) in Denmark.

Country-level indicators

We will examine the relationship between LS and ethnic minority status in the context of the following country-level variables (please see Table 2 for characteristics of these indicators at the level of countries).

Table 2.

Characteristics of Potential Moderators of the Relationship Between LS and Ethnic Minority Status.

Country	HDI^a	Country’s population size^b	Former Communist country^c	Ethnic fractionalization^d
Albania	0.749	2,831,741	1	.220
Belgium	0.897	11,094,850	0	.555
Bulgaria	0.782	7,327,224	1	.402
Czech Republic	0.873	10,505,445	1	.322
Cyprus	0.848	862,011	0	.094
Denmark	0.901	5,580,516	0	.082
Estonia	0.846	1,325,217	1	.506
Finland	0.892	5,401,267	0	.132
France	0.893	65,287,861	0	.103
Germany	0.920	81,843,743	0	.168
Hungary	0.831	9,931,925	1	.152
Iceland	0.906	319,575	0	.080
Ireland	0.916	4,582,707	0	.121
Israel	0.900	7,412,200	0	.344
Italy	0.881	59,394,207	0	.115
Kosovo	0.714	1,798,645	1	.574
Lithuania	0.818	3,003,641	1	.322
Netherlands	0.921	16,730,348	0	.105
Norway	0.955	4,985,870	0	.059
Poland	0.821	38,538,447	1	.118
Portugal	0.816	10,542,398	0	.047
Russia	0.788	143,056,383	1	.245
Slovakia	0.840	5,404,322	1	.254
Slovenia	0.892	2,055,496	1	.222
Spain	0.885	46,818,219	0	.417
Sweden	0.916	9,482,855	0	060
Switzerland	0.913	7,954,662	0	.531
Ukraine	0.740	45,453,282	1	.474
The United Kingdom	0.875	63,495,303	0	.121

Note. LS = life satisfaction; HDI = Human Development Index.

HDI 2012: Data were retrieved from United Nations Development Programme, 2013 Human Development Report (Table 1: HDI and its components; https://data.undp.org).

Data are retrieved from the European Social Survey Multilevel Data (n.d.).

Here, “1” refers to the transition or post-Communist countries, whereas “0” refers to non-post-Communist countries (“List of socialist states,” n.d.).

Data were retrieved from Alesina, Devleeschauwer, Easterly, Kurlat, and Wacziarg (2003).

Human Development Index (HDI)

We retrieved each country’s HDI of the year 2012 from the 2013 Human Development Report (United Nations Development Programme, https://data.undp.org). Among the 29 ESS6 countries, HDI ranged from 0.714 (in Kosovo) to 0.955 (in Norway).

Former Communist country

We used a binary variable to differentiate between countries that were former Communist states (“1”) and countries that were not (“0”; see “List of socialist states,” n.d.). Altogether, there were 12 countries (41.4%) in our sample with a history of Communist rule in the 20th century.

Population size

Countries’ population sizes were retrieved from the ESS Multilevel Data (n.d.). These ranged from 319,575 (in Iceland) to 143,056,383 (in Russia). The median population size was 7,954,662.

Ethnic diversity of countries

We used the ethnic fractionalization score by Alesina, Devleeschauwer, Easterly, Kurlat, and Wacziarg (2003) as a measure of countries’ ethnic diversity. The higher score reflects the higher probability that two randomly selected individuals from a population belonged to different ethnic groups (Alesina et al., 2003). Please note that this score is probably somewhat outdated for some of the European countries in our study, where the population has undergone remarkable changes in the last decade. In some countries (such as Norway and the Netherlands), the drastically increased immigration has led to much greater heterogeneity, whereas in others (such as Estonia), the population has become more ethnically homogeneous due to extensive emigration. Unfortunately, we were unable to find any comparable index of countries’ ethnic diversity that would employ more up-to-date information.

Results

The Influence of Ethnic Minority Status on LS

First, we examined whether there are any differences between mean LS judgments across ethnic majority and minorities. Please note that post-stratification weight (including design weight) was applied in all the following analyses. In the ESS6 total sample, LS judgments were indeed significantly lower for the ethnic minorities than for the majority, t(56,541) = 22.69, p < .001; see Figure 1. The effect size, Cohen’s d (for groups with different sample sizes) was 0.38, which can be interpreted as a moderate effect size.

The largest effect sizes were found in Slovakia (Cohen’s d = 0.74) and Czech Republic (Cohen’s d = 0.67). Moderate effect sizes (the values of Cohen’s d from 0.40 to 0.60) were in Lithuania, Bulgaria, Denmark, Belgium, Slovenia, Estonia, and Hungary, and there were 10 countries (Sweden, Ukraine, Netherlands, Switzerland, Spain, Portugal, Finland, Israel, Ireland, and Iceland) with relatively small effect size (i.e., Cohen’s d was from 0.20 to 0.39). However, the standardized difference between ethnic minority and majority LS was very small or non-existent (i.e., Cohen’s d < 0.20) in the United Kingdom, Norway, Poland, Kosovo, Cyprus, Germany, Albania, Russia, and France. The ethnic minority group had significantly higher LS than the majority only in Italy (Cohen’s d = −0.32). However, it should be noted that in Italy the sample of ethnic minorities in the ESS6 was also the smallest (n = 21).²

Differences in Individual-Level Covariates

Ethnic minorities were significantly different from the ethnic majority group in many ways. Across all countries, ethnic minorities’ mean self-rated health status (M = 3.70, SD = 0.98) was significantly worse than the health status of the ethnic majority group (M = 3.80, SD = 0.92), t(56,756) = 6.75, p < .001 (Cohen’s d for different sample sizes = 0.11). Ethnic minorities’ mean perceived social support (M = 4.73, SD = 1.37) was significantly lower than in case of the ethnic majority (M = 5.01, SD = 1.22), t(56,347) = 13.42, p < .001 (Cohen’s d = 0.22). There were significantly more currently unemployed individuals among ethnic minorities (15%) than among the ethnic majority (8%), χ² = 186.55, df = 1, p < .001. And, there were also significantly more ethnic minorities (17%) than majority members (1%) who considered themselves belonging to a group discriminated against on the basis of race, nationality, or ethnic group, χ² = 5,373.69, df = 1, p < .001.

The gender proportions were similar in the ethnic minority and majority sample (51% and 53% were females, respectively), χ² = 1.30, p = .26. However, there were significant differences in age and education years. More specifically, ethnic minorities were significantly younger (M = 43.90, SD = 17.57) than those who said that they were the ethnic majority (M = 47.21, SD = 18.91), t(56,730) = 10.59, p < .001 (Cohen’s d = 0.18). Also the mean number of completed education years among ethnic minorities was significantly lower (M = 11.81, SD = 4.12) than the mean of the ethnic majority (M = 12.51, SD = 4.04), t(56,419) = 10.38, p < .001 (Cohen’s d = 0.17).

Due to the finding of several significant sociodemographic and psychosocial differences (see above) between the majority and minority ethnic groups, those factors will be analyzed as covariates while examining the association between LS and ethnic minority status in the multilevel analyses, to see whether the LS differences across ethnic minority and majority individuals (if there are any) can be explained by these basic differences between ethnic groups.

Multilevel Analysis

To find out whether there is important country-level variance in the ratings of LS, and whether the strength of the relationship between LS and ethnic minority status varies significantly across countries, we used hierarchical linear modeling (HLM 6.02; Raudenbush & Bryk, 2002). First, an unconditional model (Model 0) of LS indicated that 17.8% of the variance in LS was between countries, and 82.2% of variance lied within countries. Next, we started to add predictors of LS to Level 1. All the Level 1 predictors throughout the multilevel analyses were added to the models group-mean centered (Enders & Tofighi, 2007).³ When ethnic minority status was added as a predictor of LS to the individual level (Level 1) of the model (Model 1), a significant negative relationship emerged, β = −.55, SE = 0.10, t(28) = −5.96, p < .001. The ethnic minority group belonging accounted for 1.24% of the individual-level variance in LS. The variance component estimate of the ethnic minority slope was .23 and significant, χ²(28) = 227.39, p < .001. In other words, the results showed that there were indeed differences across countries in the strength of the relationship between LS and belonging to a minority or a majority group. Next, we added more individual-level predictors as covariates to the Level 1 model to take into account the influence of perceived discrimination, current unemployment, perceived social support, and subjective health, as well as gender, age, and education level on the relationship between LS and ethnic minority status (Model 2). In the context of these variables (please note that only gender and education were not significantly related to the LS judgments; see Table 3 for detailed results), the effect of ethnic minority status on LS still remained significant, β = −.25, SE = 0.09, t(28) = −2.92, p < .01. Thus, it was appropriate to analyze the impact of between-country moderators of the LS–ethnic-minority relationship in multilevel analyses, and country-level moderators were added to Level 2 of the intercept as well as slope models.

Table 3.

The Relationship Between LS and Ethnic Minority Status in the Context of Country-Level Variables: Results From Six HLMs.

	Fixed		Random
	Coefficient (SE)	T ratio	Variance component	χ²
Model 1: Intercept of LS, γ₀₀	6.86 (0.18)	37.23***	1.02	11,600.60***
EM status, β₁	−0.55 (0.10)	−5.54 ***	0.23	227.39***
Model 2: Intercept of LS, γ₀₀	6.83 (0.19)	36.00***	1.08	14,204.86***
Predictors of LS at L1:
EM status, β₁	−0.25 (0.09)	−2.92 **	0.16	156.97***
Perceived discrimination, β₂	−0.51 (0.12)	−4.35***	0.24	60.33***
Unemployed, β₃	−1.01 (0.06)	−17.59***	0.07	99.48***
Social support, β₄	0.36 (0.03)	22.39***	0.01	136.04***
Subjective health, β₅	0.65 (0.02)	27.14***	0.01	137.09***
Age, β₆	0.004 (0.002)	2.40*	0.00	199.88***
Gender, β₇	0.04 (0.03)	1.21	0.02	78.85***
Education, β₈	0.01 (0.01)	1.14	0.00	174.78***
Moderator models of LS–EM status relationship at L2^a
Model 3: Intercept of LS, γ₀₀	6.83 (0.12)	57.88***
For LS intercept: HDI, γ₀₁	0.62 (0.08)	7.89***	0.42	5,538.18***
For EM status slope: HDI, γ₁₁	0.15 (0.07)	2.16 *	0.14	143.30***
Model 4: Intercept of LS, γ₀₀	6.83 (0.15)	46.50***
For LS intercept: Ex-Communist country, γ₀₁	−0.82 (0.19)	−4.35***	0.66	8,483.26***
For EM status slope: Ex-Communist country, γ₁₁	−0.54 (0.13)	−4.25 ***	0.13	128.17***
Model 5: Intercept of LS, γ₀₀	6.83 (0.19)	35.34***
For LS intercept: Population size, γ₀₁	0.10 (0.07)	1.44	1.13	14,732.03***
For EM status slope: Population size, γ₁₁	0.12 (0.06)	1.95	0.13	120.18***
Model 6: Intercept of LS, γ₀₀	6.83 (0.18)	37.91***
For LS intercept: Ethnic fractionalization, γ₀₁	−0.23 (0.11)	−2.10*	0.98	12,840.60***
For EM status slope: Ethnic fractionalization, γ₁₁	−0.23 (0.05)	−4.84 ***	0.09	98.84***

Note. Post-stratification weight (including design weight) was applied. The fixed coefficients and t-values in bold indicate the association between LS and ethnic minority status (at Level 1), and the statistically significant (p < .05) moderators of the association between LS and ethnic minority status (at Level 2). LS = life satisfaction; HLM = hierarchical linear modeling; EM Status = ethnic minority status; L1 = Level 1 or within-country level of the model; L2 = Level 2 or between-country level of the models; HDI = Human Development Index.

At the same time, at Level 1, LS is predicted by perceived discrimination, unemployment, social support, subjective health, and age.

p < .05. **p < .01. ***p < .001.

In the following models, the individual-level (Level 1) relationship between LS and ethnic minority status (while taking into account other significant individual-level covariates—perceived discrimination, current unemployment, perceived social support, and subjective health, and age) was modeled by the following country-level (Level 2) moderators: countries’ HDI, former Communist country status, countries’ ethnic fractionalization (i.e., ethnic diversity), as well as population size. All of the four country-level moderators were standardized and entered into the Level 2 (or the between-country level) of the models. All Level 2 variables were grand mean centered, whereas the centering of Level 1 predictors (i.e., group-mean centering) remained unchanged.⁴ At first, each country-level moderator was entered into a separate HLM model. There were thus altogether four hierarchical models with a single Level 2 moderator (Models 3-6; Table 3).

As can be seen in Table 3, countries’ HDI, ex-Communist status, and ethnic fractionalization had a significant impact (p < .05) on the LS–ethnic minority relationship. The effect of population size was not significant (p = .061). There were no significant problems related to multicollinearity among Level 2 variables—HDI had the highest variance inflation factor (2.19) and tolerance statistic (0.46) across the country-level indicators, but these values do not indicate to collinearity-related problems (Kutner, Nachtsheim, & Neter, 2004). Thus, all statistically significant moderators (i.e., countries’ HDI, ex-Communist status, and ethnic fractionalization) were next added together into a single model (Model 7). Results showed that countries’ higher ethnic fractionalization led to greater negative influence of ethnic minority status on LS (γ = −.21, SE = 0.04, t = −5.02, p < .001). The negative relationship between LS and ethnic minority status was also significantly stronger in former Communist countries (γ = −.52, SE = 0.15, t = −3;.47, p < .01; Table 4). The effect of countries’ HDI, however, became non-significant when the other two country-level factors were taken into account.

Table 4.

The Relationship Between LS and Ethnic Minority Status in the Context of Country-Level Variables: Results From Model 7 (HLM).

	Fixed		Random
	Coefficient (SE)	T ratio	Variance component	χ²
Model 7: Intercept of LS, γ₀₀	6.83 (0.12)	58.96***	0.42	5,303.09***
Moderators of LS–EM status relationship at L2^a:			0.09	93.87***
For LS intercept: HDI, γ₀₁	0.58 (0.10)	6.09***
For LS intercept: Ex-Communist country, γ₀₂	−0.10 (0.18)	−0.53
For LS intercept: Ethnic fractionalization, γ₀₃	−0.05 (0.09)	−0.51
For EM status slope: HDI, γ₁₁	−0.09 (0.09)	−1.00
For EM status slope: Ex-Communist country, γ₁₂	−0.52 (0.15)	−3.47 **
For EM status slope: Ethnic Fractionalization, γ₁₃	−0.21 (0.04)	−5.02***

Note. Post-stratification weight (including design weight) was applied. The fixed coefficients and t-values in bold indicate the statistically significant (p < .05) moderators of the association between LS and ethnic minority status (at Level 2). LS = life satisfaction; HLM = hierarchical linear modeling; EM status = ethnic minority status; L2 = Level 2 or between-country level of the models; HDI = Human Development Index.

At the same time, at Level 1, LS is predicted by perceived discrimination, unemployment, social support, subjective health, and age.

p < .05. **p < .01. ***p < .001.

Discussion

Ethnic diversity is increasing in most advanced countries, driven mostly by sharp increases in immigration (Putnam, 2007), including the ongoing and growing refugee crisis in Europe. Thus, it is important to examine whether and how the perceived ethnic minority status influences individuals’ satisfaction with life. Analyses based on nationally representative ESS data from 2012 (the sixth wave) showed that, across Europe, ethnic minorities are generally less satisfied with their lives than the ethnic majority members.

It is reasonable to ask whether the possible link between ethnic minorities’ lower satisfaction with life could be their difficult life situation, knowing that many minorities deal with the consequences of discrimination, unemployment, and poorer health on daily basis. The results of our study support earlier research findings that have shown that ethnic minority groups tend to face discrimination (Kirmanoglu & Baslevent, 2014) and unemployment (Leping & Toomet, 2008) more frequently than the majority population. Similarly to earlier studies (e.g., Ferrada-Noli et al., 1995; Nielsen & Krasnik, 2010), minorities also reported poorer health and perceived fewer possibilities for receiving help and support than the members of ethnic majority groups. However, even if all these relevant factors described above were taken into account as covariates, the general negative effect of ethnic minority status on being satisfied with life still remained significant. This means that the LS disparities between ethnic majority and minority groups are not easily explained away and there must be profounder reasons for ethnic minorities to be less satisfied with life than individuals who belong to the countries’ majority nation. It might be important that, in our study, individuals with ethnic minority status had self-identified themselves as belonging to an ethnic minority group. The term minority may, however, be perceived as carrying a negative connotation, because in people’s minds being a minority is often associated with being poor and uneducated, or even a kind of a second-class citizen. Thus, it can be speculated that individuals of foreign descent who are dissatisfied with their life and feel detached from the larger society are also more likely to identify themselves as ethnic minorities compared with more successful members of ethnic minority groups.

In addition to the difficult socioeconomic conditions, discrimination, and deteriorated physical or psychological health that are often associated with ethnic minority status, there is another issue that deserves attention when analyzing the LS levels of ethnic minorities in comparison with the majority group. Namely, ethnic minorities, most notably immigrants, may come from a context with a completely different frame of reference for LS.⁵ The frame-of-reference bias refers to differences in the way respondents formulate their answers to survey questions, based on their own life experiences as well as their knowledge about the experience of others, including both those they consider as within their “comparison group” and those outside it (Beegle, Himelein, & Ravallion, 2012). In case of immigrants, it is possible that these individuals have come from a country with rather low LS and their own LS may actually have increased in comparison with their home country’s average level of LS. Yet, their LS may still be significantly lower than that of the majority group in their host country. Clearly, framing effects may influence the size of group and country differences observed in SWB data (OECD, 2013) and create somewhat ambiguous comparisons between ethnic majority and minority groups. But as explained earlier, ethnic minority status does not equal being an (first-generation) immigrant or a foreign-born individual and thus, cannot explain the finding that minorities’ lower LS was nevertheless relatively robust across Europe.

Having said that, the magnitude of the disparity in ethnic minorities’ and majority’s LS ratings did depend somewhat upon the specific country of residence. Whereas in several countries, such as Germany, France, Russia, and Norway, the ethnic minority status was not bound to significantly reduced LS ratings as compared with the ethnic majority, the differences in LS were relatively large in Slovakia and the Czech Republic, and 17 other countries of the ESS6. Hierarchical multilevel models showed that the variation in the relationship between LS and ethnic minority status was most significantly moderated by countries’ ethnic fractionalization, which reflects countries’ ethnic diversity, as well as by the legacy of Communist rule—ethnic minority status had a stronger negative influence on LS in countries with higher ethnic fractionalization as well as in countries with Communist past (even though more than 25 years have passed since). According to Putnam (2007), immigration and ethnic diversity are likely to have important cultural, economic, fiscal, and developmental benefits in the long run. In the short run, however, immigration and ethnic diversity tend to reduce social solidarity (Putnam, 2007). The differences in the level of LS between ethnic minority and majority groups is probably larger in countries where the presence of overall social problems (unemployment, income gap, gender inequality, etc.) might more severely impair well-being of the most vulnerable social groups, including the ethnic minorities. The legacy of Communism appears to be one of those factors that add to the satisfaction disparity between ethnic minority and majority groups. A possible explanation to this is related to the change of the status of the Russian-speaking community. Formerly Russians had had the highest status in many of these countries, but since independence (when the annexation into Soviet Union was publicly acknowledged and condemned) they suddenly became illegal immigrants and colonizers (Ehala, 2009). Understandably, this was not easily accepted by Russians and created insecurity in the Russian-speaking community. Tensions between ethnic minority groups and the majority (such as the events related to the relocation of a World War II monument in Estonia in 2007) are believed to reflect perceived threat to ethnic identities caused by rapid changes (Ehala, 2009). In addition to identity and status-related issues, Russians were in these countries also profoundly affected in socioeconomic spheres—reorganization of the economy included restructuring industries that were formerly represented mostly by Russians. At the same time, Russians lacked native language proficiency, which began to influence individuals’ prospects in the job market (Kus, 2014). Thus, it is possible that in ex-Communist countries the LS of ethnic minorities is lower due to their changed position in society, diminished resources, or perceived discrimination.

Countries’ human development level was not a significant moderator of the relationship between ethnic minority status and LS, if countries’ ethnic fractionalization and post-Communist status were taken into account. This might suggest that despite living in highly developed countries, the ethnic minorities may still suffer from lower LS than the ethnic majority. Life in highly developed countries might still be difficult for some social groups who have fewer opportunities to effectively participate in society (in terms of labor market access, education, health, etc.). Countries with very high human development often have rather high levels of social equality and justice, but research shows that in some wealthy countries (such as Ireland and Spain) there is still room for improvement (see Schraad-Tischler & Kroll, 2014). When the disparity between ethnic minorities’ socioeconomic conditions and that of the majority population is large, then the perceived inequality could result in minorities’ dissatisfaction and even psychological disturbances. This is in accordance with studies describing high incidence of mental health problems in ethnic minority groups in Western Europe (Missinne & Bracke, 2012; Veling et al., 2008). However, it should also be noted that in countries where socioeconomic development is in general relatively modest, people are in general less satisfied with their lives, regardless of their ethnic background.

Against our expectations, also the size of the population was not a significant moderator of the relationship between ethnic minority status and LS. We also expected that larger populations of countries would be related to greater ethnic diversity. However, in our data, countries’ population size was unrelated to ethnic diversity. This could be explained by our approach to ethnic diversity—the country-level scores of ethnic fractionalization used in this study reflected the probabilities that two randomly selected individuals from countries’ populations belonged to different ethnic groups (Alesina et al., 2003). Namely, in countries with very large populations (e.g., Russia, Germany, and the United Kingdom), the ethnic fractionalization scores were rather modest. This is by far not because these countries are ethnically homogeneous, but rather because the ethnic majority population is so numerous that it outweighs the ethnic minority fractionalization to a significant degree. It could be one of the reasons why in the largest countries of the ESS Round 6, the negative effects of ethnic minority status on LS seemed to be absent. The other possible explanation could be taken from Fischer’s (1975) subcultural theory of urbanism: Large population size should increase the likelihood that any individual will be able to find others to who he or she is similar, and thus to be understood by others.

Limitations and Conclusions

It is important to note that ethnic minorities are rather disproportionately involved in the ESS6 countries’ samples, although one of the key principles of the ESS is national representativeness. This is, however, a larger problem in social survey research (Laganà, Elcheroth, Penic, Kleiner, & Fasel, 2013). Cross-sectional and longitudinal surveys have shown systematically higher non-response and attrition rates for immigrants and ethnic minorities (Deding, Fridberg, & Jakobsen, 2008; Laganà et al., 2013). The more an ethnic minority group differs from the majority socioculturally, the less likely it is represented in its proper proportion in the major national surveys (Lipps, Laganà, Pollien, & Gianettoni, 2011). This is related to methodological difficulties, such as language problems (cf. Missinne & Bracke, 2012). The ESS questionnaire has to be translated into a minority language, if it is spoken as a first language by at least 5% of the population (ESS, 2012). This means, however, that ethnic minorities are especially underrepresented in countries with many different ethnic groups—there might be numerous minority languages, but if they do not reach the translation threshold, they will not be used in the interview. There is another limitation related to the ESS ethnic minority samples—namely, we could not distinguish between specific ethnic/national minority groups within each country in terms of minorities’ immigration background, status, or position in society. Unfortunately, in several countries the ethnic minority sample sizes were too small for further groupings to be made.

And last, one of the drawbacks of this study could be using a single-item measure of LS. Although according to some authors, single-item measures of LS have adequate convergent validity and satisfactory reliability (see Lucas & Donnellan, 2012), interpretations that are based on a single item is nevertheless somewhat problematic. Comparisons and analyses of scores are acceptable and yield meaningful interpretations only if the scale is measuring the same trait in all the countries. Thus, when gathering and comparing data from two or more cultural groups, researchers should explicitly evaluate measurement invariance and distinguish between different levels of similarity or equivalence. One of the main methods to test measurement invariance is multiple-group confirmatory factor analysis (CFA; Milfont & Fischer, 2010), which requires that latent factors are measured with at least three indicators (Byrne, 1998). As in the ESS6 questionnaire the overall LS is measured by just a single item, we were not able to conduct the necessary invariance testing.

To sum up, this study showed that satisfaction with life was in general considerably lower for ethnic minorities than for the ethnic majority groups in Europe, even if individuals’ perceived discrimination, current unemployment, social support, and self-rated health, as well as age, gender, and education level were taken into account. However, in several countries this tendency did not appear. The negative influence of ethnic minority status on LS appears to be enhanced in countries with Communist past as well as in countries, which have greater ethnic diversity. These findings have implications for understanding the sociopsychological situation of ethnic minorities in today’s Europe, and should be of special interest to the social policy makers in Eastern European countries, where the gap between ethnic groups’ LS is the largest. More specifically, the results from this study suggest that to achieve the benefits of ethnic diversity and to better the well-being of minorities, the policy makers of many countries should focus on increasing social solidarity by promoting positive inter-ethnic interactions. A critical step is finding balance between encouraging cultural retention and promoting adaptation to the larger society—this could be facilitated by considering the attitudes and perceptions of immigrants and other ethnic minorities (Phinney, Horenczyk, Liebkind, & Vedder, 2001), and by providing them real opportunities to feel being more integrated into society. These findings, especially in the light of the current refugee crisis in Europe, are also relevant for highly developed Western European countries: SWB includes elements that transcend economic prosperity (Diener et al., 1999), and ranking in the top of the Human Development Report does not ensure high LS for country’s ethnic minority members.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Preparation of this article was supported by the University of Tartu (SP1GVARENG) and by institutional research funding (IUT2-13) from the Estonian Ministry of Education and Science. Anu Realo was supported by a grant from the Netherlands Institute for Advanced Study in the Humanities and Social Sciences (NIAS) during the preparation of this article. Financial support from NORFACE research program on “Migration in Europe—Social, Economic, Cultural and Policy Dynamics” is also acknowledged.

Notes

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