International Journal of Psychology, 2015 DOI: 10.1002/ijop.12149

Happier together. Social cohesion and subjective well-being in Europe Jan Delhey1 and Georgi Dragolov2 1 Institute

for Sociology, Otto von Guericke University Magdeburg, Germany of Humanities and Social Sciences, Jacobs University Bremen, Germany, and Bremen International Graduate School of Social Sciences (BIGSSS)

2 School

D

espite mushrooming research on “social” determinants of subjective well-being (SWB), little is known as to whether social cohesion as a collective property is among the key societal conditions for human happiness. This article fills this gap in investigating the importance of living in a cohesive society for citizens’ SWB. For 27 European Union countries, it combines the newly developed Bertelsmann Foundation’s Cohesion Index with individual well-being data on life evaluation and psychological functioning as surveyed in the recent European Quality of Life Survey. The main results from multi-level analyses are as follows. First, Europeans are indeed happier and psychologically healthier in more cohesive societies. Second, all three core domains of cohesion increase individuals’ SWB. Third, citizens in the more affluent part of Europe feel the positivity of social cohesion more consistently than those in the less affluent part. Finally, within countries, cohesion is good for the SWB of resource-rich and resource-poor groups alike. Our findings also shed new light on the ongoing debate on economic progress and quality of life: what makes citizenries of affluent societies happier is, in the first place, their capacity to create togetherness and solidarity among their members—in other words, cohesion. Keywords: Social cohesion; Subjective well-being; Happiness; Psychological functioning; Affluence; Inequality; European Quality of Life Survey.

Over the past 20 years, there has been growing interest in subjective well-being (SWB) as an indicator of quality of life. In contrast to economists, social psychologists and sociologists have always stressed the role of non-economic factors for well-being, such as partnership, friendship, trust, membership in voluntary associations and civic engagement (e.g. Argyle, 1999; Haller & Hadler, 2006). Only recently did “relational goods” and social capital gain in importance for economists as well (Layard, 2005). The literature sometimes discusses these “social factors” under the umbrella term social cohesion. Most of these studies involve purely individual-level research (e.g. Klein, 2013), which mainly informs on the well-being effects of individual social resources. However, they do not enlighten us on the relevance of social cohesion as a contextual condition. The term cohesion as used in social

psychology and sociology always denotes a collective quality—a characteristic of a group, neighborhood, region or society. Cohesion itself is not, and cannot be, a characteristic of individual societal members. Aggregate interpersonal trust levels and association membership rates have been used as proxies for cohesion as a collective property (e.g. Bjornskov, 2003), but to our knowledge there has been no attempt to assess the well-being effect of cohesion, comprehensively measured. With this article, we explicitly acknowledge the collective nature of social cohesion, and apply appropriate methodology to investigate its effect on individual SWB—multilevel analysis. Inspired by the Easterlin paradox (cf. Easterlin, 2001), much of the cross-national comparative research on societal conditions for happiness is preoccupied by wealth and economic growth (Diener & Kahnemann, 2009). In

Correspondence should be addressed to Jan Delhey, Institute for Sociology, Otto von Guericke University Magdeburg, Zschokkestr. 32, D-39104 Magdeburg, Germany. (E-mail: [email protected]). We are heavily indebted to Klaus Boehnke, Zsófia Ignacz and Jan Lorenz for developing with us the Bertelsmann Cohesion Index at Jacobs University. We also would like to thank Kai Unzicker (Bertelsmann Stiftung) and two anonymous reviewers for their comments on an earlier version of this article. Financial support for this research from the Bertelsmann Foundation is gratefully acknowledged.

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contrast, social cohesion has received much less attention. One of the reasons probably is that, until recently, there was no summary measure readily available which allows for comparisons across a larger number of countries. This has changed with Bertelsmann Foundation’s Cohesion Radar project.1 Our study combines its newly developed Cohesion Index (Bertelsmann-Foundation, 2013) with individual well-being data for the 27 European Union member states from the 2011/2012 European Quality of Life Survey (EQLS). Our main goal is to investigate the contribution of social cohesion to individual SWB, in comparison to affluence and income inequality. The evidence suggests that Europeans are happier and psychologically better-off in cohesive societies; this effect is most pronounced in richer countries. Finally, cohesion benefits vulnerable and less-vulnerable social categories of people more or less equally. We first introduce our main concepts and explain why cohesion can be expected to enhance SWB. We then describe our data, measures and methodology; this section also provides descriptive information on the levels of social cohesion and well-being across Europe. The article then proceeds to the empirical evidence on the cohesion-SWB-link. The last section discusses the main findings and identifies further questions not addressed here. Theoretical framework and key concepts The sequence model of life evaluation Our theoretical framework builds on the sequence model of life evaluation (Veenhoven, 2012). In evaluating life as a whole, people draw cognitively on perceptions of how their life is, compared to their ideal of how it should be, and emotionally on the balance of positive and negative emotions. These cognitions and emotions are a reaction to various life events, bigger and smaller, that a person encounters in daily life. The events themselves depend systematically on a person’s life chances, under which Ruut Veenhoven subsumes individual capabilities, personal resources and external conditions, the latter including large-scale societal conditions. Relevant conditions might be economic (national wealth and unemployment rate), political (rule of law and political freedom), social (degree of inequality and social cohesion) or cultural (e.g. value climate). Our key argument is that a cohesive society, as defined below, is a crucial societal condition for a positive life evaluation and SWB in general. We presume that living in a cohesive environment triggers more positive and less negative life events, other things being equal. These more positive day-to-day experiences improve the affect balance and shift cognitive representations of the social environment towards 1 See

www.social-cohesion.net.

judgments such as “nice,” “rewarding” and “livable.” As a result, people are happier and more satisfied with life and achieve higher psychological well-being. But what exactly is social cohesion? Social cohesion Although being an “old” concern of sociologists since Durkheim, social cohesion has emerged as a “new” topic in social reporting and academic quality-of-life research in the 1990s. It is often assumed that cohesion is under pressure these days in Western societies, due to the recent economic crisis and trends such as globalisation, growing economic inequality and immigration. In the public discourse, social cohesion is generally valued in and for itself, as it reflects solidarity and social harmony. It is also regarded as an important collective resource for economic success and individual quality of life. Yet, very little is known about the instrumental value flowing from cohesion. There is some evidence that component parts of cohesion are good for SWB (see below), but this research typically relies on single proxies for social cohesion, rather than a comprehensive measurement. Over the past 20 years, research on cohesion has been mainly concerned with defining what cohesion is. Only recently did scholars start to meter how cohesive contemporary societies are (Bertelsmann-Foundation, 2013; Dickes & Valentova, 2013). Research has also attempted to determine the country conditions that strengthen or weaken cohesion (e.g. Bertelsmann-Foundation, 2013; Janmaat, 2011). On the one hand, there are the more theoretically inspired conceptions, such as Lockwood’s (1964) idea of cohesion as social integration. Other scholars have identified the constitutive elements of cohesion more pragmatically, such as the influential study by Chan, To, and Chan (2006). At times, the variety of definitions has been heavily criticised. Yet, one should not overlook the considerable overlap between them, which has been carved out in a recent screening study (Schiefer, van der Noll, Delhey, & Boehnke, 2012). To begin with, cohesion is consensually seen as referring to a specific aspect of a collective quality of life: the social glue. This focus renders cohesion more specific than social quality (e.g. Walker & Van der Maesen, 2004), but broader than social capital. Further, whereas social capital is applicable to individuals, cohesion is not. Most scholars also agree on the multi-dimensional and graduated nature of cohesion. In substantive terms, social relations and networks, social and political trust, tolerance, civicness, participation and the absence of conflicts are regarded as key components of cohesion (Chan et al., 2006; Dickes & Valentova, 2013; Janmaat, 2011). We base this article on a definition that builds in many ways on this consensus: “A cohesive society is characterised by resilient social relationships, a positive © 2015 International Union of Psychological Science

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Figure 1. Domains of social cohesion and their respective dimensions (Source: Eurofound & Bertelsmann Siftung, 2014).

emotional connectedness between its members and the community, and a pronounced focus on the common good” (Bertelsmann-Foundation, 2013, p. 11). Social relations, in this context, are understood as the horizontal network that spans among individuals and groups within a society. Connectedness refers to the vertical ties among individuals, their country and institutions. A focus on the common good, finally, is reflected in the actions and attitudes of the members of society that demonstrate responsibility for others and for the community as a whole. This approach adds an important aspect, the common good commitment. We argue that this component is neither entirely “horizontal” (“social,” “informal” and “attitudinal”) nor entirely “vertical” (“political,” “formal” and “behavioral”), important criteria other scholars have used to classify key domains. This is why we distinguish it as a separate, third domain. Figure 1 illustrates the concept with its three core domains, which further unfold into nine dimensions. These dimensions offer a considerable overlap with previous concepts. Yet, one frequently used component that our approach does not consider is value consensus (e.g. Janmaat, 2011). Our concept prefers tolerance towards people who lead different lifestyles to a value consensus which potentially excludes non-mainstream groups (cf. Chan et al., 2006). Subjective well-being SWB is a multi-faceted concept, too. One can broadly differentiate between transitory and enduring well-being states. The former comprise emotional well-being (Nettle, 2005)—the positive and negative affects that individuals typically experience in daily life. Among the enduring © 2015 International Union of Psychological Science

well-being components, the literature mainly differentiates between life evaluation and psychological functioning. Life evaluation is a hedonic orientation defined as happiness in terms of life satisfaction (Veenhoven, 2012). The more recently established concept of psychological functioning is concerned with perceived thriving vis-à-vis the encounter of existential challenges (Ryan & Deci, 2001; Ryff, 1989). There is no established operationalisation. Ryff identifies six dimensions: self-acceptance, positive relations with others, environmental mastery, autonomy, purpose in life and personal growth. Ryan and Deci consider only autonomy, positive relations and, yet another dimension, competence (vis-à-vis valued goals in life); Diener and Biswas-Diener (2009) add optimism about the future, among other indicators. Four decades of SWB research have produced a rich body of empirical evidence on who is happy and satisfied, and why (cf. Argyle, 1999; Headey & Wearing, 1992). At the individual level, a number of factors are important, from genetics and personality types over resources to life styles and life arrangements. As to contextual factors, the livability of communities and societies has been shown to influence SWB. A recent review lists affluence, rule of law and well-functioning public institutions, personal freedom, equality, tolerance and interpersonal trust as conducive conditions to life satisfaction (e.g. Veenhoven, 2012). Not all of these factors work universally, though. For example, equality appears to be more important for Europeans/Westerners than for others (Delhey & Dragolov, 2014). Clearly, social cohesion falls under the contextual conditions of SWB, as illustrated above with the sequence model of life evaluation.

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Cohesion and SWB: developing hypotheses Research on trust climates points towards a positive effect of trust on SWB (Bjornskov, 2003; Calvo, Zheng, Kumar, Olgiati, & Lisa, 2012; Helliwell & Wang, 2011), and trust is a key indicator of cohesion. On the other hand, societies rich on associational life do not appear to have happier citizens (Bjornskov, 2006), and membership density is also a key indicator of cohesion. In this light, the debate about the negative aspects of social capital such as social control, restriction of personal freedom, downward levelling norms and rent-seeking (Graeff, 2009) gains relevance. Similarly, social cohesion might be a mixed blessing, too. This calls for a more systematic investigation into the impact of cohesion, comprehensively measured. At the same time, it is important to investigate whether all three main components of social cohesion contribute to SWB. Thus, our first two hypotheses read: H1: Living in a cohesive society enhances individuals’ well-being. H2: All three domains of cohesion contribute to individuals’ well-being.

Studying the cohesion-SWB link might be particularly fruitful for Europe’s richer part. The main issue here is the well-known marginal utility of income and national wealth. Wilkinson and Pickett (2010) claim that we witness the end of an era in which economic progress goes hand-in-hand with higher quality of life (they suggest that equality gains relevance instead). We expect social cohesion to be more important for well-being in richer societies, in line with arguments about the post-materialisation of happiness—the idea of a relative shift in happiness recipes, away from economic concerns towards non-economic ones (Delhey, 2010; Welzel & Inglehart, 2010). Indeed, aggregate studies revealed a social capital index and aggregate trust to be more conducive to average life satisfaction among richer countries (Bjornskov, 2003; Ram, 2010). Therefore, we hypothesise: H3: Social cohesion is more important for citizens of richer countries.

We further aim to examine whether social cohesion is equally good for everybody. We hypothesise that vulnerable groups—resource-poor categories of people such as the poor, the unemployed, the elderly—benefit more from living in a cohesive society than resource-rich people do. The vulnerable groups may draw more on collective resources, among them the social glue cohesion provides, which partly makes up for their lack of individual resources. In contrast, resource-rich groups may depend to a lesser extent on their social environment

to achieve well-being, and therefore may be more sensitive to the potential dark sides of cohesion, such as restricted autonomy. H4: Social cohesion is more beneficial for the subjective well-being of vulnerable groups.

METHODOLOGY AND DATA Multilevel regression The abundant literature on happiness points to various personal characteristics with a non-negligible effect on SWB. At the same time, national socio-economic characteristics such as affluence and the income distribution affect both social cohesion (Bertelsmann-Foundation, 2013) and SWB (e.g. Delhey & Dragolov, 2014). In order to adequately control for possible confounding effects—countries’ socio-economic conditions and population composition—we use multilevel regression. This method treats variables at the macro level more appropriately, offering estimates and standard errors based on the number of level-two observations (here N 2 = 27) rather than on the total sample size (here N 1 = 34,891). Hypotheses 3 and 4 require testing for moderation effects. We opt for splitting the total sample into the subsamples of “rich” and “less rich” countries, and resource-rich and -poor individuals, respectively. This way we achieve fully interacted models that are more straightforward to interpret in comparison to models with interaction terms. Moreover, our hypotheses refer substantially to the so-called simple effects of an interaction, which are directly accessible from our approach. Data and variables Macro-level predictors Data on the degree of social cohesion stem from the Cohesion Radar project (Bertelsmann-Foundation, 2013). This social reporting initiative compared 34 advanced societies, among them the EU-27 members and further 7 OECD countries in four time periods between 1989 and 2012. The measurement of the nine cohesion dimensions (see Figure 1) follows a reflective index-building approach and has been therefore performed in a factor analytical framework. The measurement of social cohesion in the time period 2004–2008 used 47 indicators from established international social surveys and social statistics from international bodies. Table A4 in the Appendix lists exemplary indicators for the nine dimensions.2 The measurement of the three domains of cohesion and the overall cohesion index assumes, in contrast, a formative index-building approach in taking

2 The complete list of indicators is available in Table 5 of the Appendix of the international comparison (Bertelsmann-Foundation, 2013: pp. 68–69).

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the average of the respective dimensions. Further details on the cohesion index can be found in a methods report.3 Figure 2 depicts cohesion’s strength for the 27 EU member states (years 2004–2008). Individual-level data stem from the third round of the EQLS, which primarily relate to the year 2011. To allow for a time lag between the hypothesised predictor (cohesion) and the outcome variables (SWB), we consider country standings on social cohesion, national affluence and income inequality for the years covered by the third wave of the Cohesion Radar, namely 2004–2008. Data on countries’ wealth, expressed with the natural logarithm of gross domestic product (GDP) in purchasing power parity, stem from the World Bank’ webpage, data on income inequality as measured with the Gini coefficient for net equalised household income from Solt’s database (2009). As we deal with a time period spanning 5 years, we average yearly data on each of the two characteristics. We thus have Luxembourg as the richest country and Romania as the poorest. Denmark turns out to be the most equal nation and Portugal the most unequal. In one step of the analysis, we work with subsets of more affluent and less affluent societies. As the Spirit-Level controversy shows, there is no agreed-upon threshold. In our view, what matters is not so much passing a certain arbitrary income level recently, but rather the experience of more encompassing existential security over a longer time period. The classification of “first world countries” by Milanovic (2005) is quite helpful in this context. It counts the “old” EU-15 members as rich/secure. Consequently, the 12 accession countries are considered here as poorer. Individual-level variables EQLS 3 offers cross-sectional representative samples of the population aged 18 and above in 34 European countries (including Turkey). The overlap with the Cohesion Radar involves the EU-27 member states. Owing to the excellent data quality, the occurrence of missing values is negligible and we therefore do not see the need to use multiple imputation as this method is still not well developed for multilevel designs. Instead, we applied listwise deletion for the nominally scaled variables which only slightly reduced the original sample size from 35,516 to 34,891 cases, our total working sample. As the occurrence of missing data on the continuous variables ranged between 1 and 2%, thereby nowhere exceeding the 3% rule of thumb, we substituted missing values with the respective country means. Our working country sample sizes thus range between 910 in Luxembourg and 2,999 in Germany. 3 See

5

The EQLS provides tried-and-tested item batteries for the measurement of SWB. Life evaluation is measured as happiness and life satisfaction: “Taking all things together … , how happy would you say you are” and “All things considered, how satisfied would you say you are with your life these days.” Both have a response scale from 1 (very unhappy or very dissatisfied, respectively) to 10 (very happy or very satisfied, respectively). The two items correlate strongly and significantly at r (34,889) = .64, p < .01. Cronbach’s alpha4 is sufficiently high at .78. We therefore subsume happiness and life satisfaction under life evaluation by taking their arithmetic mean for each individual. The operationalisation of psychological functioning was attempted with the following items: “I am optimistic about the future” (optimism), “I generally feel that what I do in life is worthwhile” (purpose), “I feel I am free to decide how to live my life” (autonomy), “In my daily life, I seldom have time to do the things I really enjoy” (engagement), “Life has become so complicated today that I almost can’t find my way” (environmental mastery), “I feel close to people in the area where I live” (positive relations). All items have a response scale from 1 (strongly agree) to 5 (strongly disagree). They do not match fully any particular operationalisation of psychological functioning, but they overlap considerably with the inventories suggested by Ryff (1989), and by Diener and Biswas-Diener (2009). We reversed all but the item tapping environmental mastery so that a higher numerical code stands for better functioning. Factor analysis reveals one component with an eigenvalue higher than one (1.27) that underlies, however, only the co-variation of optimism, purpose and autonomy; Cronbach’s alpha is at .65 for these three items. We thus take their average. The individual-level correlation between life evaluation and psychological functioning is at .49 (p < .01). This suggests that two distinct aspects of well-being are captured rather than overlapping ones, as the literature on flourishing suggests (Huppert & So, 2013). Figure 3 presents country averages on the two SWB components. Individual-level controls to account for composition effects We control for various personal characteristics that are known to influence well-being: gender (female = 1); age and its quadratic effect (both continuous); marital status, with the married and cohabiting respondents forming the reference category as compared to the separated, widowed and divorced on one hand, and singles on the other;

http://www.gesellschaftlicher-zusammenhalt.de/en/downloads/. judge the sufficiency of the size of Cronbach’s alpha coefficient of scale consistency, we follow the relative threshold suggested by Nunnally (1967), which takes into account the length of a scale, i.e. the number of items measuring the latent construct: an alpha of .10 times the number of indicators is considered sufficient. 4 To

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Social Cohesion Denmark Finland Sweden Ireland Luxembourg Austria Netherlands United Kingdom Belgium France Spain Germany Malta Cyprus Slovenia Portugal Italy Estonia Poland Hungary Slovakia Czech Republic Greece Latvia Romania Bulgaria Lithuania

DK FI SE IE LU AT NL UK BE FR ES DE MT CY SI PT IT EE PL HU SK CZ GR LV RO BG LT -1.3

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Figure 2. Social cohesion and its three domains in time period 2004–2008.

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Life Evaluation

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Psychological Functioning

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Figure 3. Country average scores on life evaluation and psychological functioning, 2011/2012.

education as measured with the International Standard Classification of Education (ISCED) on a scale of 0 (no completed education) to 6 (advanced level of tertiary education); employment status, with the employed serving as a reference group in comparison with the unemployed, the retired and a third group of homemakers, students and others; and how easily people make ends meet as measured on a scale that we reversed such that 1 stands for “with great difficulty” and 6 for “very easily.”5 Descriptive information on all variables is given in Table A1 of the Appendix. RESULTS Small to medium context effects for well-being The so-called empty model in multilevel regression gives information on the percentage of total variation in a 5 This

dependent variable that pertains to the societal context, known as the intra-class correlation ρ. We find medium-sized context effects for life evaluation (ρ = .11) and psychological functioning (ρ = .13). Country differences up to 64% due to population composition In a next step, we specified the individual part of our separate models for each SWB component. Coefficient estimates are available in Columns “I” of Tables 1 and 2. The effects of these individual-level controls are, by and large, consistent. Our proxy variable for income, how easy it is to make ends meet, shows the strongest positive impact on life evaluation and psychological functioning.6 The composition of the population explains 46% of the country differences in psychological functioning and 64% in life evaluation.

variable serves as a proxy for income because the EQLS income variable contains many missing values. that we deal with unstandardised regression estimates. To assess the relative importance of a predictor, we could standardise the variables (not done here), but also calculate impact in terms of the largest possible difference a predictor can make on the dependent variable. For the variable tapping the ease of making ends meet, the difference between its highest and lowest value is 5. Hence, its impact on life evaluation is a 2.4 unit change (.48 × 5) on the scale of life evaluation (1 to 10). 6 Note

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DELHEY AND DRAGOLOV TABLE 1 Multi-level regression of life evaluation

Notes: GDP = gross domestic product; ISCED = International Standard Classification of Education; I = individual level; M = macro-level characteristics; M+C = M and cohesion; M+D1 = M and domain 1; M+D2 = M and domain 2; M+D3 = M and domain 3. *p < .10. **p < .05. *** p < .01 (two-tailed); ref.: reference category.

Social cohesion positively associated with well-being To test Hypothesis 1, we introduce cohesion as well as the two macro-level controls, country wealth and income inequality. Tables 1 and 2 summarise the evidence (Column “M+C”). Cohesion significantly improves life evaluation and increases psychological functioning. The maximum impact of social cohesion on life evaluation (scale 1 to 10) can be a change of 1.23 units, on psychological functioning (scale 1 to 5) a change of .97 units. Thus, psychological functioning is more strongly influenced by cohesion than happiness and life satisfaction.7 Individuals who live in more cohesive societies are more optimistic about the future, have a stronger feeling that their lives are purposeful, and feel greater freedom to decide how to live their lives. The results further point to an insignificant influence of income inequality on SWB, once cohesion has been taken into account. Prior to introducing cohesion (Column “M” of Tables 1 and 2), income inequality had a significant negative effect on psychological functioning, which disappears when social cohesion is taken into account. This suggests a mediation: inequality weakens cohesion, 7 See

the previous footnote on how to calculate impact.

and cohesion decreases psychological well-being. With respect to prosperity, a similar story can be told for life evaluation. Europeans are happier and more satisfied with life in richer countries, but when cohesion is added, it fully absorbs (mediates) the enhancing effect of affluence on life evaluation. For psychological functioning, taking cohesion into account turns national affluence from a neutral into a negative condition; individuals living in more affluent societies of similar levels of cohesion report lower, not higher, psychological well-being. All social cohesion domains are conducive to SWB For testing Hypothesis 2, we specify separate multilevel models for each cohesion domain, again controlling for country affluence and income distribution. The results are provided in Tables 1 and 2 (Columns “M+D1,” “M+D2” and “M+D3”). In line with the effect of overall cohesion, all three cohesion domains are conducive to SWB. No matter whether countries score high on social relations, connectedness or common good orientation, their inhabitants have higher SWB. This congruence in performance underscores the idea that the three domains are facets of the same underlying social phenomenon. Yet, the fact that the impact of the overall cohesion index is stronger © 2015 International Union of Psychological Science

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TABLE 2 Multi-level regression of psychological functioning

Notes: GDP = gross domestic product; ISCED = International Standard Classification of Education; I = individual level; M = macro-level characteristics; M+C = M and cohesion; M+D1 = M and domain 1; M+D2 = M and domain 2; M+D3 = M and domain 3. *p < .10. **p < .05. ***p < .01 (two-tailed); ref.: reference category.

suggests that none of the three domains alone is sufficient to capture social cohesion in its complexity.

We differentiate along the following lines (the more vulnerable group comes second):

Social cohesion more important for well-being in richer countries

• Gender—men versus women; • Age—young (18–34 years old) versus old (65 and above); • Chronic illness—no versus yes; • Employment status—employed versus unemployed; • Income situation—(very) easy versus (very) difficult to make ends meet.

To test Hypothesis 3, we perform separate multilevel regressions for the less affluent and more affluent societies. We do not additionally control for GDP in these analyses because the grouping considerably reduces the range of wealth and limits the number of countries to 12 and 15, respectively. Table 3 presents the evidence.8 The positive influence of cohesion on life evaluation holds for both country groups: no matter whether Europeans live in richer or poorer societies, cohesion is conducive to their life satisfaction and happiness. In contrast, individuals’ psychological functioning benefits from strong cohesion only in the richer countries. Thus, the positive impact of cohesion is felt more clearly and systematically in Europe’s richer part. Social cohesion is good for all We finally compare the effect of social cohesion on the SWB of resource-rich and resource-poor people (H4). 8 We

The variable with respect to which we formed these contrast groups has been taken out of the respective analyses. The results—the effect of cohesion on either life evaluation or psychological functioning in the social category studied—are summarise d in Figures 4 and 5. Full results are presented in Tables A2 and A3 of the Appendix. Consistently, we find positive and significant effects of social cohesion on both SWB components in each and every group, above and beyond the effects of other individual and societal characteristics (again, affluence and inequality) that we controlled for. Everybody benefits from cohesion: be it male or female, young or old, economically active or unemployed, healthy or less healthy, well-off or poor. There is no noticeable difference in the

repeated these analyses for other splits into rich and poor, producing similar results. Evidence is available upon request from the authors.

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DELHEY AND DRAGOLOV TABLE 3 Multi-level regression of life evaluation and psychological functioning by affluent and less-affluent country Life evaluation

Level: Country Gini Cohesion Level: Individual Female Age (years) Age (quadratic effect) Marital status: married Separated/widowed/divorced Single Education level (ISCED) Employment status: employed Unemployed Retired Other employment status Easy-difficult to make ends meet Intercept Slope variance (intercept) Residual variance (intercept) N 2 (country) N 1 (individual)

Psychological functioning

AC-12

EU-15

AC-12

.03 .68***

.00 .49***

.02** .13

.11*** −.07*** .00*** ref. −.54*** −.47*** .08*** ref. −.50*** −.06 −.01 .57*** 6.50*** .05*** 2.79*** 12 13,725

.12*** −.03*** .00*** ref. −.60*** −.47*** .04*** ref. −.63*** .12*** −.16*** .39*** 6.78*** .02*** 2.37*** 15 21,166

EU-15 .00 .42***

.01 −.02*** .00*** ref. −.00 −.02 .04*** ref. −.14*** .00 .01 .17*** 3.06*** .01*** .51*** 12 13,725

.01 −.01*** .00*** ref. −.02* −.03** .03*** ref. −.18*** .06*** .01 .15*** 3.37*** .01*** .45*** 15 21,166

Notes: ISCED = International Standard Classification of Education. *p < .10. **p < .05, ***p < .01 (two-tailed); ref.: reference category. .25 .20 .15

Gender

Age

Chronic illness

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.21 ***

.21 ***

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(very) easy

(Very) difficult

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Employed

Young

.00

.23 ***

Yes

.11 **

No

.18 ***

Old

.15 ***

Women

.05

Men

.10

Employment

Income

Figure 4. Standardised effect of social cohesion on life evaluation for groups of resource-rich and -poor individuals.

size of the effects between the non-vulnerable and vulnerable groups on any of the examined characteristics. Age is a partial exception, with the elderly benefiting more in terms of happiness and life satisfaction from living in a cohesive society. But even younger people are happier in more cohesive countries.

collective nature of social cohesion and have investigated the significance of this specific societal condition for individual SWB. Our study is such an attempt, bringing together the new Bertelsmann Cohesion Index and survey data on life evaluation and psychological functioning for 27 European countries. Multilevel analyses have revealed the following new insights:

SUMMARY AND DISCUSSION

1. Over and above national affluence and the income distribution, social cohesion enhances Europeans’ SWB. It generates better psychological functioning (strongest effect) and boosts happiness and life satisfaction.

Whereas numerous studies exist that link individuals’ social relations, trust and other social resources to well-being outcomes, few studies have acknowledged the

© 2015 International Union of Psychological Science

Age

Chronic illness

.33 ***

.29 ***

.33 ***

.32 ***

(very) easy

(Very) difficult

.33 ***

Unemployed

.33 ***

Employed

Young

Gender

.34 ***

Yes

.30 ***

No

.34 ***

Old

.28 ***

Women

.4 .35 .3 .25 .2 .15 .1 .05 0

Men

HAPPIER TOGETHER—COHESION AND SWB

Employment

11

Income

Figure 5. Standardised effect of social cohesion on psychological functioning for groups of resource-rich and -poor individuals.

2. All three domains of social cohesion—social relations, connectedness and common good orientation— are conducive to SWB. 3. The positivity of cohesion is felt more strongly in Europe’s richer part (the Western part). In the poorer part (largely ex-communist Eastern Europe), cohesion enhances life evaluation, but not psychological functioning. 4. Both the more vulnerable and the less vulnerable social categories benefit from living in a cohesive society. Cohesion is good for all. That social cohesion turns out to be such a significant contextual condition reinforces the message from numerous individual-level studies: social factors are the key to leading a happy and fulfilling life. As we could not find any detrimental effect, cohesion deserves to be promoted by public policy. However, strengthening social networks, trust and tolerance (social relations), increasing citizens’ national identification, trust in major institutions and sense of social fairness (connectednesss) and motivating people to show solidarity, obey social rules and engage in the civil society (common good orientation) are challenging policy goals. The finding that SWB depends more consistently on cohesion in affluent societies is in line with theories on the post-materialisation of happiness (Delhey, 2010). As cohesion is exactly about the quality of social life, the differential impact of cohesion can be interpreted within a post-modernisation framework. Findings that West Europeans typically score higher than East Europeans on post-materialist and self-expression values (Inglehart & Welzel, 2005) strengthen this interpretation. An interesting question for future research is to what extent this pattern is indeed due to prosperity, or rather to the individualistic culture prosperity tends to produce. Another open issue is whether cohesion improves more transitory aspects of SWB, such as positive and negative © 2015 International Union of Psychological Science

emotions. Last but not least, evidence from other world regions is needed. Overall, the benefits flowing from social cohesion outweigh those from national wealth and income equality. Social cohesion, rather than just affluence, matters for well-being and increasingly so when the economy advances. Moreover, social cohesion was not found to have any detrimental effect on SWB, neither for the average citizen nor for any of the sub-groups considered. Does this render discussions on the dark sides of cohesion superfluous? Not necessarily. The Bertelsmann Cohesion Index is explicitly based on a “modern” notion of social bonds that stresses inclusive-universalistic forms of solidarity, rather than exclusive-particularistic ones. Expressions of the latter type of cohesion might well have their downsides and consequently lower SWB. Another important finding is that inequality appears to be less of a burden for Europeans. It is rather national wealth and social cohesion that shape well-being more strongly. This is in contrast to what the Spirit-Level theory suggests. However, we analysed inequality in terms of income distribution only, whereas other inequalities may be more relevant to people’s SWB. Finally, our findings shed new light on the ongoing debate about economic progress and quality of life. What makes the citizenries of affluent societies happier and psychologically better-off is, in the first place, their capacity to create togetherness and solidarity—in other words, cohesion. Beyond this, affluence appears to add little, if anything, to individual happiness and life satisfaction. Psychological functioning is even reduced by the purely materialistic component of affluence (the non-overlapping part with cohesion), much in line with the idea of affluenza (James, 2008) as a social ill. If this were to be true, making societies richer is conducive to SWB mainly in so far that it strengthens social cohesion. At the end of the day, we are happier together. Manuscript received May 2014 Revised manuscript accepted January 2015

12

DELHEY AND DRAGOLOV

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Huppert, F. A., & So, T. T. C. (2013). Flourishing across Europe: Application of a new conceptual framework for defining well-being. Social Indicators Research, 110, 837–861. Inglehart, R., & Welzel, C. (2005). Modernization, cultural change and democracy. The human development sequence. New York, NY: Cambridge University Press. James, O. (2008). The selfish capitalist. Origins of affluenza. London: Vermilion. Janmaat, J. G. (2011). Social cohesion as a real-life phenomenon: Assessing the explanatory power of the universalist and particularist perspectives. Social Indicators Research, 100, 61–83. Klein, C. (2013). Social capital or social cohesion: What matters for subjective well-being? Social Indicators Research, 110, 891–911. Layard, P. R. G. (2005). Happiness. Lessons from a new science. London: Penguin Books. Lockwood, D. (1964). Explorations in social change. London: Routledge & Kegan Paul. Milanovic, B. (2005). Worlds apart: Measuring international and global inequality. Princeton, NJ: Princeton University Press. Nettle, D. (2005). Happiness. The science behind your smile. Oxford: Oxford University Press. Nunnally, J. C. (1967). Psychometric theory. New York, NY: McGraw-Hill. Ram, R. (2010). Social capital and happiness: Additional cross-country evidence. Journal of Happiness Studies, 11(4), 409–418. Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52, 141–166. Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57, 141–166. Schiefer, D., van der Noll, J., Delhey, J., & Boehnke, K. (2012). Cohesion Radar: Measuring Cohesiveness. Social Cohesion in Germany – a Preliminary Review. Gütersloh, Germany: Bertelsmann Foundation. Solt, F. (2009). Standardizing the world income inequality database. Social Science Quarterly, 90, 231–242. Veenhoven, R. (2012). Happiness, also Known as "Life Satisfaction" and "Subjective Well-Being". In K. C. Land, A. C. Michalos, & M. J. Sirgy (Eds.), Handbook of social indicators and quality of life research (pp. 63–77). Dordrecht, the Netherlands: Springer-Verlag. Walker, A., & Van der Maesen, L. (2004). Social quality and quality of life. In W. Glatzer, S. von Below, & M. Stoffregen (Eds.), Challenges for quality of life in the contemporary world. Advances in quality-of-life studies, theories and research Vol. (Vol. 24 pp., pp. 13–32). Dordrecht, the Netherlands: Kluwer Academic. Welzel, C., & Inglehart, R. (2010). Agency, values, and well-being: A human development model. Social Indicators Research, 97(1), 43–63. Wilkinson, R., & Pickett, K. (2010). The spirit level: Why more equal societies always do better. London: Penguin Books.

© 2015 International Union of Psychological Science

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APPENDIX TABLE A1 Descriptive information on variables used. Min Level: Country (N 2 = 27) Gini ln(GDP) Cohesion Domain 1: social relations Domain 2: connectedness Domain 3: common good Level: Individual (N 1 = 34,891) Life evaluations Psychological functioning Female Age (years) Age (quadratic effect) Marital status: married Separated/widowed/divorced Single Education level (ISCED) Employment status: employed Unemployed Retired Other employment status Easy-difficult to make ends meet

Mean

23.63 9.23 −1.30 −1.47 −1.39 −1.83 1 1 0 18 324 0 0 0 0 0 0 0 0 1

SD

Max

29.79 10.11 −.18 −.17 −.14 −.24

4.18 .43 .73 .84 .74 .84

7.21 3.75 .57 50.90 2915.64 .60 .23 .17 3.08 .45 .07 .32 .16 3.57

1.84 .77 d 18.02 1878.25 d d d 1.34 d d d d 1.28

37.50 11.16 1.31 1.48 1.54 1.23 10 5 1 95 9025 1 1 1 6 1 1 1 1 6

Notes: d = dummy variable; GDP = gross domestic product; ISCED = International Standard Classification of Education; SD = standard deviation. TABLE A2 Multi-level regression of life evaluation for groups of resource-rich and -poor individuals. Gender

Level: Country by wave Gini index ln(GDP) Cohesion index Level: Individual Female Age (years) Age (quadratic effect) Marital status: married Separated/widowed/divorced Single Education level (ISCED) Employment status: employed Unemployed Retired Other employment status Easy-difficult to make ends meet Intercept Slope variance (intercept) Residual variance (intercept) N 2 (country) N 1 (individual)

Age

Chronic illness Old

Men

Women

Young

.01 .07 .42***

.01 −.08 .51***

.00 −.24 .27**

.02* .18 .68***

.01 −.12 .49***

.01 .06 .63***

.01 −.15 .46***

.04

.12***

−.06***

−.04***

.00*** ref. −.67*** −.47*** .05*** ref. −.70*** .00 −.22*** .47*** 6.14*** .05*** 2.42*** 27 14,849

.00*** ref. ref. −.50*** −.64*** −.46*** −.31*** .06*** .05*** ref. ref. −.45*** −.61*** .05 −1.80*** −.03 .18*** .48*** .39*** 7.52*** 8.46*** .04*** .04*** 2.66*** 2.29*** 27 27 20,042 7683

.08*** −.05*** .00*** ref. −.55*** −.40*** .05*** ref. −.54*** .12*** .04 .43*** 7.96*** .03*** 2.30*** 27 24,003

.16*** −.03*** .00*** ref. −.56*** −.59*** .06*** ref. −.67*** −.08 −.31*** .48*** 5.20*** .07*** 2.99*** 27 10,888

ref. −.49*** −.33*** .04*** ref. −.15 −.22* −.31** .45*** 3.38*** .05*** 2.79*** 27 9117

No

Yes

Employment Employed Unemployed

Income situation Easy

Difficult

.00 −.58* .72***

.01 −.29** .50***

−.00 .07 .64***

.05** −.06*** .00*** ref. −.56*** −.45*** .05*** ref.

.25*** −.06*** .00*** ref. −.59*** −.56*** .05 ref.

.05 −.02*** .00** ref. −.59*** −.34*** .05*** ref. −.46*** .15*** −.05

.18*** −.07*** .00*** ref. −.64*** −.57*** .09*** ref. −.73*** −.03 −.25***

.42*** 8.53*** .04*** 2.15*** 27 15,789

.56*** 12.10*** .11*** 3.26*** 27 2537

11.03*** .03*** 2.17*** 27 8351

7.62** .12*** 3.78*** 27 6427

Notes: The table shows unstandardised estimates from multi-level regression. GDP = gross domestic product; ISCED = International Standard Classification of Education. Significance of the estimates in the case of two-sided tests: *p < .10. **p < .05. ***p < .01. ref.: reference category.

© 2015 International Union of Psychological Science

14

DELHEY AND DRAGOLOV TABLE A3 Multi-level regression of psychological functioning for groups of resource-rich and -poor individuals. Gender

Level: Country by wave Gini index ln(GDP) Cohesion index Level: Individual Female Age (years) Age (quadratic effect) Marital status: married Separated/widowed/divorced Single Education level (ISCED) Employment status: employed Unemployed Retired Other employment status Easy-difficult to make ends meet Intercept Slope variance (intercept) Residual variance (intercept) N 2 (country) N 1 (individual)

Age

Chronic illness

Employment

Men

Women

Young

Old

No

Yes

Employed Unemployed

−.00 −.36*** .33***

−.00 −.47*** .40***

−.00 −.50*** .35***

.00 −.28*** .39***

−.00 −.45*** .38***

−.00 −.38*** .38***

−.00 −.49*** .36***

.00

.04**

−.02*** .00*** ref. −.07*** −.05** .03*** ref. −.18*** .00 −.01 .16*** 7.36*** .01*** .47*** 27 14,849

−.01*** .00*** ref. .02 −.02 .03*** ref. −.14*** .05*** .03* .16*** 8.46*** .02*** .48*** 27 20,042

ref. −.02 .01 .01* ref. −.17*** −.19 .09*** .16*** 8.49*** .01*** .46*** 27 7683

ref. −.04** −.02 .04*** ref. .11 −.06 −.08 .14*** 5.99*** .02*** .45*** 27 9117

−.00 −.01*** .00*** ref. .01 −.02 .03*** ref. −.16*** .06*** .05*** .15*** 8.20*** .02*** .45*** 27 24,003

.04** −.01*** .00*** ref. −.04*** −.05** .04*** ref. −.16*** .00 −.03 .15*** 7.32*** .02** .53*** 27 10,888

Income situation Easy

Difficult

.00 −.59*** .42***

−.00 −.41*** .34***

.01 −.57*** .50***

−.01 −.02*** .00*** ref. .01 −.02 .03*** ref.

.03 −.02** .00 ref. .01 −.09** .05*** ref.

.05** −.02*** .00*** ref. −.03 −.04 .04*** ref. −.20*** −.01 -.01

.16*** 8.78*** .01*** .44*** 27 15,786

.18*** 9.23*** .03*** .63*** 27 2537

−.01 −.01*** .00 ref. .00 .00 .04*** ref. −.14*** .06** .00 8.55*** .01*** .38*** 27 8351

9.49*** .02*** .68*** 27 6427

Notes: The table shows unstandardised estimates from multi-level regression. GDP = gross domestic product; ISCED = International Standard Classification of Education. Significance of the estimates in the case of two-sided tests: *p < .10. **p < .05. ***p < .01. ref.: reference category.

TABLE A4 Exemplary indicators used for measuring the nine cohesion dimensions. Cohesion dimension Social networks Social trust Acceptance of diversity Identification Trust in institutions Perception of fairness Solidarity and helpfulness Respect for social rules Civic participation

Exemplary indicator How often socially meet with friends, relatives or colleagues? Trust in most people Would not like to have neighbor: immigrants/foreign workers How attached to country? Confidence in police Tensions between the rich and the poor (reversed) Unpaid voluntary work through community and social services Feel safe walking alone at night Volunteered time to organization

The complete list of indicators is available in Table 5 of the Appendix of the international comparison (Bertelsmann-Foundation, 2013, pp. 68–69).

© 2015 International Union of Psychological Science