Drivers of Wellbeing

For policymakers or individuals to increase wellbeing depends on them understanding what might influence or drive wellbeing. Subjective wellbeing may be influenced by a wide range of individual and societal factors. The relative importance of different drivers is likely to vary across countries and across different groups within society depending on their circumstances. The seven contributions below use ESS data to explore a number of potentially important drivers of wellbeing across European countries: work, gender, parenthood, migration, the environment, democratic government and cultural values.

Feeling balanced? Perceived work-life balance and working conditions in Europe

Satisfaction with work-life balance depends on working hours, autonomy and flexibility.  Analysis of the European Social Survey (ESS) demonstrates how many of these factors – and hence feelings of work- life balance – vary systematically across different employment regimes in Europe.

Introduction

We focus here on an important aspect of wellbeing: work-life balance.  Increasingly work-life balance has been identified as a key element of quality of work1 (e.g. Kalleberg, 2011) and quality of life (e.g  Anderson et al., 2009; Eurofound, 2012) and has been identified as an important indicator in attempts to ‘move beyond GDP’ in assessing social and economic progress (e.g. Stiglitz, Sen and Fitoussi, 2009).  Work-life balance has also become a critical issue in academic and policy debates in Europe (European Commission, 2008). Rising female employment, lone parenthood, falling fertility and an ageing population have all brought the issue of reconciling work and caring demands to the fore.  Conflict between the demands of work and family life has been linked to poorer outcomes in marital relations and child development and also to lower job satisfaction, absenteeism and stress (Allen et al., 2000).

One prominent debate in this vein of research is the extent to which work-life balance is influenced by pressures from work and pressures from home (Gallie and Russell, 2009; Jacobs and Gerson, 2004). The literature on institutional variations in advanced capitalist societies draws our attention to ways in which the demands of work and family may be mediated by the forms of production and welfare regimes in these societies. Different countries organise work in different ways, both in terms of working time and the quality of work (Gallie, 2007). A large body of work has also shown variation in how countries organise supports for non-work activities like caring and measures to support the reconciliation of work and family life (Gornick and Meyers, 2003; Plantenga and Remery, 2005).  A key advantage of the harmonised cross-country data in surveys such as the European Social Survey (ESS) is that the role of these institutional differences can be examined. The ESS has provided the basis for extensive analysis of the drivers and consequences of work-family conflict2 (see McGinnity and Whelan, 2009; McGinnity and Russell, 2013; Gallie, 2013b).  Building on this work, we here address three linked research questions:

• How do working conditions vary across country groups?
• How is satisfaction with work-life balance related to conditions in the workplace, after accounting for demands and resources within the household?
• Does institutional variation in working conditions help us to understand cross-national differences in satisfaction with work-life balance?

What is work-life balance?

Work-life balance focuses attention on how individuals integrate paid work with the rest of their life (McGinnity, 2014). Much research in the area is rooted in the notion of a conflict between roles, i.e that meeting demands in one domain makes it difficult to meet demands in another.  Working conditions can play an important role in determining how easy it is to manage that conflict: the demands-resources model distinguishes between work role demands – which may contribute to work-life conflict – and resources, which are factors used to cope with demands and reduce work-life conflict (Voyandoff, 2005).

One way to measure work-life balance in surveys such as the ESS is to ask people directly how satisfied they are with the way in which they manage the conflicting demands of different aspects of their lives.  This study uses the following measure of work-life balance, included in Rounds 3 (2006/2007), 5 (2010/2011) and 6 (2012/2013) of the ESS:

And how satisfied are you with the balance between the time you spend on your paid work and the time you spend on other aspects of your life?

This measure refers to the overall level of contentment with how one handles work and other life demands. It captures perceptions and evaluations of an unspecified level of balance rather than the level of balance itself (Drobnic, 2011). As with other subjective measures of wellbeing it will also be related to individual expectations and adaptation.

Exploring work-life balance cross-nationally using the ESS

A module of questions focusing on ‘Work, Family and Wellbeing’ was fielded as part of the ESS in Round 2 (2004/2005) and Round 5 (2010/2011).   For further information on the development of the module and the topics covered see here:

The analysis that follows uses ESS Round 5 data from 18 countries3, focusing on EU countries and Norway, where most research has been done to develop typologies comparing institutional conditions.  The analysis is restricted to employees of working age defined as 20-64 years. Analysis of work-life balance is restricted to those who are living with a partner to allow analysis of the role of household employment patterns in work-life balance, a key theme in the literature.4 The data is weighted using the European Labour Force Survey to be representative of the working-age population (see Gallie, 2013a: pp. 28-29 for details).

We draw on employment regime theory and previous empirical research on the nature of working conditions and welfare support across countries (Gallie, 2013a) to identify five country groupings or regimes across which to compare working conditions and work-life balance.

• Liberal: UK, Ireland
• Nordic: Denmark, Norway, Sweden, Finland
• Continental: Germany, Netherlands, Belgium
• Southern: Greece, Spain, Portugal
• Transition: Estonia, Poland, Hungary, Slovakia, Czech Republic, Slovenia

Differences in working conditions across countries

Using the country groupings outlined above we examine key working conditions across different employment regimes (Table 1 and Figure 1).5 The pattern of paid working hours in different regimes varies by gender.6 Table 1 shows that paid working hours are highest for men in the Transition and Southern countries. Paid working hours for women are lowest in the Continental and Liberal regimes, where part-time work is common, and highest in the Nordic, Transition and Southern countries where a higher proportion of women work full-time.7

Job pressure shows little variation by country group, but job control and flexible scheduling (being able to decide own start/finish times) are significantly higher in Nordic and Continental countries compared with other countries. The proportion of individuals who feel their job is insecure is much lower in the Nordic and Continental countries. The proportion of employees who feel their health and safety is at risk is lowest in the Nordic countries and considerably higher in the Transition countries (see Figure 1).

Table 1: Working hours, job pressure and job control by country group

Satisfaction with work-life balance and working conditions

What are the implications of this variation in working conditions for work-life balance? We explore this relationship by estimating a series of linear regression models to identify what predicts individuals’ responses on the scale of satisfaction with work-life balance. The analytical strategy here is to examine, firstly, how working conditions are related to perceived work-life balance, and, secondly, how far any regime differences observed in satisfaction with work-life balance can be explained by these differences in working conditions. We first present findings on how satisfaction with work-life balance is related to working conditions.

When we estimate a model of factors associated with work-life balance (see Appendix Table A.1), we find working hours are very strongly associated: those who work longer hours are much less satisfied with the balance between work and family life. Employer-driven flexibility, in particular working overtime at short notice, but also working unsocial hours like evenings, nights and weekends, reduces satisfaction with work-life balance. Employee control over start and finish times has a positive impact on satisfaction with work-life balance, albeit modest.

Work pressure has a very strong, significant association with perceived work-life balance: higher pressure at work is associated with lower satisfaction with work-life balance. Conversely, the model also shows that job control increases satisfaction with work-life balance, as found by Fagan and Walthery (2011). Having supportive colleagues is another resource which increases satisfaction. A final current job-related factor is job insecurity, which reduces satisfaction with work-life balance.

The full model also shows that family factors such as having a child under 6 decreases satisfaction with work-life balance among employees, while having a partner who works part-time significantly increases satisfaction. However, total number of children and total hours of housework undertaken by the respondent and their partner have no significant effect.

Satisfaction with work-life balance and employment regime

How much does work-life balance vary across employment regimes and what role do family factors, working hours and working conditions play in understanding these regime differences? Figure 2 presents results from a series of nested regression models designed to examine this. Five successive regression models were run into which additional groups of predictor variables were added to the previous model to try and explain more of the variance observed in work-life balance across the five ESS country groupings representing different employment regimes. Results from Model 1 show that without taking account of any other factors, regime differences in satisfaction with work-life balance are significant but not large. Satisfaction with work-life balance is highest in the Nordic countries, followed by Continental and Liberal countries (Model 1). Satisfaction is lowest in Transition and Southern group countries (these are not significantly different from each other). Controlling for demographic factors (Model 2) and family (Model 3) makes no difference to regime patterns. We saw previously that paid working hours are highly salient for work-life balance:  controlling for differences in working hours brings work-life balance satisfaction in Liberal countries to the same level as the Transition countries, and reduces the Nordic and Continental countries’ advantage (Model 4). Adding other working conditions halves the Nordic difference, suggesting higher job resources and lower demands account for much of the higher satisfaction observed in these countries (Model 5). The proportion of the total variation in satisfaction with work-life balance accounted for by different groups of factors (regime, demographic, family, working hours and other working conditions) is available in Appendix Table A.2.

Conclusion

Using data from the ESS module on ‘Work, Family and Wellbeing’, we find that satisfaction with work-life balance is strongly associated with working hours, autonomy, flexibility, and working unsocial hours, and that many of these factors vary systematically across employment regimes. We also find the satisfaction with work-life balance is higher among Nordic and Continental countries compared with other parts of Europe.  Analysis suggests that this is related to differences in working hours and better working conditions in these countries. Wellbeing differences across the different country groupings or regimes are much reduced when we account for these differences. Family demands are also significant but, compared with working conditions, explain less of the overall variation in satisfaction with work-family balance and cross-national variation in this.

The analysis of work-balance presented here provides only a partial exploration of wellbeing. One enduring limitation of the concept of work-life balance is that, by definition, it only focuses on those in employment; other groups with lower than average wellbeing, like the unemployed and long-term sick, are not included in the analysis. Another limitation is that the focus here is on current wellbeing and it is not possible to account for any consequences that different levels of current work-life balance may have for future wellbeing, for example, as regards family formation or career development (McGinnity, 2014).

Nevertheless, there are important insights to be gained from an examination of work-life balance in cross-national perspective as made possible by the ESS.  The findings suggest some clear lessons for policymakers and/or employers seeking to promote work-life balance.  For example: ensuring predictable working hours that are not too long, facilitating employee autonomy, the ability to decide start/finish times and working during the standard working week, are all measures likely to enhance work-life balance.

Gender inequalities and depression in Europe

Analysis of European Social Survey (ESS) data finds that more gender-equal societies promote better mental health among both men and women and reduce the gender gap in depressive symptoms.

Introduction

Mental health is an important component of wellbeing. Women are around twice as likely to report depressive symptoms and major depression as men (Van de Velde et al., 2010). However, this gender gap is not observed in all countries (Hopcroft and Bradley, 2007; Van de Velde et al., 2013) and also varies across the life span (Ferrari et al., 2013; McDowell et al., 2014).

Cross-national variation leads health sociologists to consider gender inequality as a potentially important culprit behind the higher incidence of depression in women. Because depression, lack of control, and powerlessness are related, gender stratification theory leads us to expect a substantial gender gap in depression in more gender-unequal societies.

However, the exact relationship between gender inequality and the gender gap in depression remains little understood. There have been only a handful of studies exploring this relationship empirically (Chen et al., 2005; Hopcroft and Bradley, 2007; McLaughlin, 2011; Seedat et al., 2009; Van de Velde et al., 2013) and their findings are inconsistent.  In this contribution we seek to clarify the relationship between gender inequality and the gender gap in depression by combining gender stratification theory with a life course perspective, examining how the relationship between gender and depression varies with age.

We first outline the different theoretical perspectives on how age, gender and depression may be related before going on to present the results of an empirical exploration of this relationship using cross-national data from the European Social Survey (ESS).

Age, gender and depression

There are three competing theories as to how the gender gap in depression may vary across the life course, each with different implications for how we might also expect the gender gap to vary across countries depending on the level of gender inequality.

The gender gap in depression during the reproductive years

Part of the interest in gender differences in depression across age is related to the hypothesis that sex differences in endocrine physiology are involved (Cairney and Wade, 2002) with an onset of a gender gap in depression related to hormonal changes associated with puberty and a narrowing of the gender gap after the end of the reproductive life stage. Empirical findings are rather inconsistent and may vary depending on the social context, but in general this hypothesis leads us to expect a gender gap in depression that weakens after the reproductive years irrespective of social conditions i.e. regardless of the level of gender equality or inequality in a country (Figure 1).

Most studies find that a gender gap in depression is ubiquitous across all age groups after adolescence (Angst et al., 2002; Kuehner, 2003; Piccinelli and Wilkinson, 2000; Wade, Cairney and Pevalin, 2002) but there is mixed evidence regarding the trajectory of the gender gap throughout the reproductive years. Some studies report a widening of the gender gap during the reproductive years (Grigoriadis and Robinson, 2007) while other studies report smaller gender gaps in middle adulthood (Cairney and Wade, 2002; Mirowsky and Ross, 1992; Missinne et al., 2014).

As a substantial part of gender stratification encompasses a gendered organization of reproductive activities (Chafetz, 1988), we might expect a more pronounced gender gap in depression during the reproductive years in gender unequal societies. However, we also have to keep in mind that there are indications that biological differences between women and men can more freely manifest themselves in the absence of social constraints (Schmitt, 2014; Schmitt et al., 2008). This would bring us to expect more pronounced gender differences in depression during the reproductive years in gender equal societies.

For the present research question, it suffices to note that, in contrast to some of the other theories presented here, hormonal theories lead us to expect gender differences in depression to decrease in older age in all cases.

Age graded status and cumulative disadvantage

The lack of evidence for a simple hormonal model of depression in women (Kuehner, 2003) leads some scholars to stress the gendered impact on depression of social statuses related to various life stages. There are two possibilities.

The first relates age differences in depression to the age-structured organization of statuses and roles (Clarke et al, 2011; Mirowsky, 1996; Mirowsky and Ross, 1992). This age graded status composition perspective assumes that the relationship between age and depression is best explained by life stage-related social positions – such as marriage and employment – that are associated with mental health benefits and disadvantages (Clarke et al., 2011; Mirowsky, 1996; Mirowsky and Ross, 1992) (Figure 2a and Figure 2b). Different take up of these social positions by men and women between countries may explain the cross-national variation observed in the depression gender gap.  Age, gender, and country differences in depression should, however, disappear after controlling for these life stage differences.

The second possibility, cumulative disadvantage theory (CAD) (Dannefer, 2003), stresses the path dependence of transitions in and out of work and family-related roles and their advantages and disadvantages for mental health. CAD theory suggests that the association between disadvantage and depression strengthens as life unfolds (Figure 3). Both the incidence and duration of social deprivation and traumas, as well as the interaction between different forms of deprivation and trauma, accumulate across the life course, and result in more mental health problems as people age (George, 2014, Stansfeld et al., 2011).

Combining CAD theory and gender stratification theory leads us to expect growing gender differences in depression with older age as any negative effects resulting from women’s relatively disadvantaged position accumulate. This widening gender gap will be particularly prominent in more gender-unequal societies where women’s cumulative disadvantage is greater. These accumulation processes surpass the individual occupancy of social positions and their mental health effects, and we expect to observe a gender gap of varying size across countries, even after taking individual differences in age graded status occupancy into account (Figure 4).

Maturation and adult development

Finally, some studies (George, 2014; Mirowsky and Ross, 1992) find that, after taking variation in social conditions and their accompanying losses and gains, and physical declines common in later life, into account, a negative relationship between age and depressive symptoms emerges. Maturation theory states that getting older comes with gaining life experience and people who have been confronted with social adversity learn to deal with it. There is some empirical evidence to support the idea that, with age, comes the capacity to make the best out of life (Chen and Miller, 2012; Schafer, Shippee and Ferraro, 2009; Mirowsky and Ross, 2001; Hatch, 2005).

In the context of this study, maturation theory – like hormonal theories – leads us to expects gender differences in depression to decline in older age as women (and men) in disadvantaged societies, or occupying disadvantaged positions, learn to cope with their condition and overcome their relative disadvantage (Figure 5).

To test these competing theories, we combine data from ESS Round 3 (2006/2007) and Round 6 (2012/2013) and explore age differences in depressive symptoms between men and women across 28 countries.¹ We focus on a subsample of respondents aged 21 to 90.² Depression is measured using a shortened version of the Center for Epidemiologic Studies Depression Scale – CES-D8 (Radloff, 1977) included in the ESS rotating modules on ‘Personal and Social Wellbeing’³

The national level of gender inequality was assessed using the Global Gender Gap Index (GGI) 2006 and 2012 (World Economic Forum, 2013). We additionally control in our analysis for a range of individual socio-demographic variables, besides age and gender, including employment status, education, household income and marital status at the individual level as well as country’s income distribution as measured by the World Bank’s GINI index 2006 and 2012 (World Bank, 2014). See Appendix Table A.1 for more information on how all the variables have been coded and Appendix Table A.2 for how they are distributed across countries.

Multilevel models are used to investigate the effects of age, gender and the level of gender equality in society on the prevalence of depressive feelings across ESS countries. Analysis is conducted on the combined sample of cases from 2006 and 2012 controlling for between round changes in gender equality. The modelling strategy used and pooling data across two rounds allows us to explore variation in depression both across countries at a given point in time and within countries over time (though the over time results are not discussed here).⁴

Here we test three models. The first is a baseline model showing age-related gender differences in depression and how these vary across countries. The second model includes controls for individual work and family status allowing us to test the age graded status composition hypothesis. The third model includes country level variables and tests the impact of gender inequality in society on levels of depression among men and women.

Findings

The results of our modelling of ESS data are presented below. Full results can be found in the Appendix.

Age, gender and depression across countries

Analysis of ESS data confirms the well-known gender difference in depression, with women overall reporting higher mean scores on the depression scale. In addition, we notice substantial variation in the gender gap in depression between countries.⁵

Overall, the frequency and severity of depression symptoms among both men and women increases with age (see Appendix Table A.3, Model 1) with a more substantial increase beyond the age of 70 for men, and beyond 60 for women. As the age effect is more pronounced for women, this results in a more prominent gender difference in depression among those aged 61 and older. These findings provide evidence against the hormonal and maturation theories (which would predict a decline in depression and narrowing gender gap with age), and rather suggests that the gender gap in is related to social conditions. The relationship between age, gender and depressive symptoms found in the ESS data is visualised in Figure 6.

The impact of age graded social statuses

The next step in the analysis is to control for differences in social status which might explain variation in the incidence of depressive symptoms across the sample (see Appendix Table A.3, Model 2). Familiar differences in mental health among various work and family-related social categories are confirmed, with more severe depression symptoms among the unemployed, the sick and disabled, the retired and housekeepers (not shown in Appendix Table A.3). A higher income is associated with better mental health. Furthermore, we observe fewer depressive symptoms among the married and cohabiting, as well as among those with children below the age of 12 in the household. Interestingly, we notice only a minor reduction of the overall gender gap in depression, when these individual differences in work and family-related statuses are accounted for.

To evaluate the age graded status composition hypothesis, we look at the reduction of gender differences in depression across age categories after controlling for these work and family factors (comparing Model 1 and Model 2 in the Appendix Table A.3). A substantial reduction of the gender gap in depression among those aged 61 and older can be identified. Also, among those in middle adulthood a still significant, but smaller, reduction in the gender gap is observed.

The finding that gender differences in depression are to a certain extent related to the status composition of the various age categories provides evidence for the age graded model. Further, the fact that the reduction of the gender gap in depression after controlling for social status is most pronounced among older adults provides evidence in support of cumulative disadvantage theory (which predicts that the effect of social status increases with age as any negative effects accumulate).

The impact of gender stratification

Finally we consider the impact of gender inequality at the country level on depression and look at how the gap in depressive symptoms between men and women of different ages varies between more and less gender-unequal countries (Appendix Table A.3, Model 4).

Figure 7 shows how the incidence of depressive symptoms varies by age between men and women depending on whether the country is in the highest, middle or lowest tertile on gender equality according to the Global Gender Gap Index.⁶

Overall, we notice a highly significant association between gender equality and depression; both women and men experience less depressive symptoms in more gender-equal countries. However, the positive effect of gender equality in reducing depression is more pronounced among women than men. This means that the gender gap in depression between men and women is smaller in more gender-equal countries than in less gender-equal countries.

Even in highly gender-equal countries, a significant gender gap does emerge among older adults; the result of a decline in depressive symptoms among men but not women between the ages of 50 and 70. However, the depression gap between men and women is less pronounced at all ages in more gender-equal countries compared with less gender-equal countries (and almost absent in young adults).

In countries where women face more unequal treatment, the lifelong accumulation of this disadvantage is more pronounced and the gender gap in depression which emerges with age greater. The finding confirms the complementarity of cumulative disadvantage and gender inequality theory in explaining gender differences in depression across countries and age categories.

Conclusion

Using ESS data on the incidence of depressive symptoms we have explored the relationship between age, gender and depression across different European countries. Consistent with a combination of gender stratification theory and cumulative disadvantage theory, we find that gendered opportunity structures impact the lives of women and men, and lead to gender differentiated mental health outcomes later in life.

These outcomes partially result from the age-specific occupancy of work and family-related social positions by women and men, but cannot be reduced to this. The level of gender inequality in society also has a role to play. This is an important observation. Most health sociological and social epidemiological studies focus on the individual social positions of women and men and, hence, underestimate the impact of gendered societal arrangements on depression. It is important to notice that less gender inequality goes hand in hand with better mental health for both women and men (though especially women). This has important implications for policymakers, suggesting the importance of policies to promote gender equality in different spheres including education, employment and politics.

As the CES-D8 is also part of Round 7 of the ESS (2014/2015), interested researchers will be able to investigate whether these findings can be replicated. Moreover, additional rounds of data on the prevalence of depressive symptoms among Europeans will provide opportunities to test whether any further evolution towards more gender-equal European societies results in a reduction or elimination of the gender gap in depression.

Does parenthood bring about happiness?

Analysis of European Social Survey (ESS) data suggests that whether parenthood brings happiness depends on your gender and whereabouts in Europe you live.

Introduction

Most European countries are now experiencing fertility levels well below the replacement level of two children per woman.  This decline in fertility, together with the steady increase in life expectancy, is a key driver behind the ageing of European populations, posing enormous challenges for policy makers. However, despite the general decline, fertility rates do vary considerably across European countries.  The Total Fertility Rate (TFR) i.e. the average number of children per woman ranges from only 1.3 to 1.4 in countries like Spain and Germany to as high as 1.9 to 2 in other countries such as the UK and Sweden (see Appendix Table A.1). Might a wellbeing perspective help us to explain these patterns?  Is it necessarily the case that, as societies develop, parents simply derive lower wellbeing from having children? How and why does the wellbeing associated with having children vary from one European country to another?

We argue that individuals’ subjective wellbeing matters in explaining childbearing behaviour across Europe.  Subjective wellbeing is a direct function of the discrepancy between aspirations and actual attainment (Plagnol and Easterlin, 2008). Following the broad expansion in educational opportunities for women, aspirations have clearly increased as has the importance attached to gender equality. This need not necessarily lead to a decline in fertility rates. The problem arises if societal conditions and institutional arrangements do not similarly evolve in response to these emerging preferences. If people are not able to realise their higher aspirations – for example if the institutional arrangements are not in place to allow women to combine childbearing and full-time employment successfully or if inequity is perceived in the treatment of mothers and fathers – this may lead to lower subjective wellbeing associated with having children and lower fertility levels (McDonald, 2013; Esping-Andersen, 2009). To the extent that the wellbeing associated with childbearing is influenced by the presence or lack of supportive institutional arrangements, the availability of which will vary across countries, this may help to explain differences in total fertility rates across Europe.

Using ESS data to explore fertility decisions

We use data from ESS Round 6 (2012/2013) to explore fertility decisions across Europe. To date, the effect of childbearing on individuals’ subjective wellbeing has mainly been studied with longitudinal data for a few selected countries where such data are available (e.g. Billari and Kohler, 2009; Baranowska and Matysiak, 2011; Kohler et al., 2005; Margolis and Myrskylä, 2011; Myrskylä and Margolis, 2014). Longitudinal data is useful because one can associate changes in childbearing status with changes in reported happiness for specific individuals; thereby better establish the extent in which childbearing leads to a change in subjective wellbeing.  The ESS – even though giving a cross-sectional perspective – provides a unique opportunity to test the relationship between happiness and fertility in most European countries and allows us to present a country comparison of happiness and parenthood.

As a proxy of the individual subjective well-being we use a variable from the ESS core questionnaire which asks respondents to rate their happiness on a scale from 0 to 10.

C1. Taking all things together, how happy would you say you are?

We analyse data from 27 European countries that took part in ESS Round 6 to explore the association between happiness and fertility decisions at the country level.¹ Bearing in mind that the wellbeing derived from having children may differ between mothers and fathers – and the gap between mothers’ and fathers’ wellbeing may vary across countries – we conduct separate analysis for men and women of reproductive age, defined as 20 to 50.²

Findings

The first notable finding from our analysis is that there is a clear positive relationship between the level of development in society – as measured by the Human Development Index (HDI) – and both happiness and fertility rates.

The fitted line in Figure 1 shows that, across Europe, the higher the level of development, the happier the citizens of the country are. Figure 1 also suggests that fertility is higher in those countries scoring higher in terms of their average level of development.  The size of the country markers is proportional to the Total Fertility Rate (TFR) in that country; when looking at the countries in the upper right hand corner, we see that their fertility is also higher, whereas the countries in the lower left hand corner have much lower fertility. This is an interesting finding, since it suggests that despite development being historically a strong predictor of fertility decline, when countries reach a certain threshold of development – as is generally the case in Europe relative to the rest of the world – the relationship appears to be reversed.³

We might conclude that this is a simple income effect; couples in rich countries are simply more able to afford to have children. But what about happiness? Higher income is not necessarily a guarantee of greater happiness, as the famous Easterlin paradox suggests (Easterlin, 1976): people with higher incomes are generally happier than people with lower incomes, but (assuming income above a certain minimum threshold) an increase in overall income for the population as a whole will not increase individual happiness if people’s relative economic position remain.

We can explore the relationship between development, happiness and parenthood in more detail by comparing the experiences of parents and non-parents across European countries. In Figure 2 we compare the relationship between happiness and the HDI for fathers and non-fathers whilst in Figure 3 we do the same for mothers and non-mothers. Across Europe, fathers are happier than non-fathers (Figure 2). In other words, parenthood for men, on average, appears to improve their happiness no matter where they live in Europe and regardless of the relative level of societal development.

However, for women the relationship between happiness and parenthood is less straightforward. It appears that mothers are happier than non-mothers but only in the most highly developed European countries. Where development is relatively less advanced, mothers appear to be less happy than non-mothers (Figure 3). Comparing Figure 3 and Figure 1 we see that those countries in which mothers are happier than non-mothers are also those countries in which fertility rates are higher and vice versa. In sum, there is an important relationship between development, childbearing and happiness, but the relationship differs between men and women. For women, it seems that the relationship between childbearing and happiness is dependent on societal conditions.  This may include the presence (or lack) of institutions that support the combination of childbearing and paid work.

Evidence on the importance of institutions

The same interaction between a country’s level of development and the happiness associated with motherhood is observed if we substitute the HDI measure for other measures of societal development more closely associated with gender roles and childbearing – the availability of childcare facilities or the number of women in parliament (used as a proxy measure for women’s empowerment) for example (see Aassve et al., 2014).

If anything, the relationship between societal development and maternal happiness is even stronger using these measures than when using HDI. This is consistent with the argument advanced above that subjective wellbeing depends on the correspondence between aspirations and reality and that – for many women in Europe – their aspirations centre around being able to combine having children with successful careers. In order to achieve this, two things are required: first the opportunity to outsource childcare activities, and second, a more equal sharing of household tasks among partners. The latter may be more common in countries with higher levels of female empowerment and gender equality, leading, in turn, to higher subjective wellbeing associated with childbearing among women (Mencarini and Sironi, 2012).

The relationships between parenthood, happiness and level of development demonstrated in Figures 1 to 3 are confirmed with the help of multilevel analysis which models wellbeing as a function of individual characteristics (including parenthood) and the various measures of societal development discussed above. This analysis confirms that for women – but not men – the positive effect of parenthood on wellbeing depends on the level of institutional and societal development available to facilitate the realisation of women’s growing aspirations to combine childbearing with paid employment and minimise perceptions of inequity in the treatment of men and women (see Aassve et al., 2014 for more details of this analysis).

Conclusion

Analysis using ESS data to explore the association between subjective wellbeing and parenthood across European countries demonstrates that having children is often associated with higher wellbeing but that, for women at least, this is dependent on where you live. The fact that motherhood is associated with higher wellbeing only in those countries with higher levels of societal and institutional development may help to explain the variation in fertility rates across Europe.  The total fertility rate is lower in countries such as those in southern and eastern Europe where institutional and societal arrangements are less supportive of women successfully realising their growing aspiration to combine motherhood with a career.  The fact that fathers are always happier than non-fathers – independent of a country’s institutional characteristics – suggests very clearly that gender continues to matter in critical ways and that it is predominantly mothers who lose out in countries where appropriate institutions for reconciling parenthood and work are less developed.  Policy makers seeking to increase fertility rates to address the problems associated with an ageing society should consider what institutional arrangements they might put in place to make motherhood and work more compatible.

Migrating to find happiness?

An analysis of migrants moving from eastern/central to western Europe using data from the European Social Survey (ESS) suggests that migrating to a wealthier country does not necessarily bring them happiness.

Introduction

One might reasonably expect that migrants moving to wealthier countries would improve their lives in significant ways.  Voluntary migration in particular ought to enhance wellbeing; the mere fact that migrants choose this path certainly indicates that they expect to experience improved wellbeing.

Analysis of European Social Survey (ESS) data on intra-European migration, however, suggests the possibility that migrants might not experience greater happiness after moving to a wealthier country.  Migrants’ lives might well improve in ways that indicate higher objective wellbeing, and in some circles migration is seen as an important component of development both for the migrants and for the origin country (see, for example, Cortina and Ochoa-Reza, 2013).  But, in subjective terms, it appears migration may provide significant potential for disappointment.

Income, happiness, and implications for migrants

The possibility that migration may not lead to greater happiness is rooted in a well-known idea about the relationship between income and happiness: income does matter for happiness, but mainly via the way income signals status (as against income’s function as a means for acquisition).  Income’s relative dimension is what influences our happiness.  As per the ‘Easterlin paradox’, people with higher incomes are happier than people with lower incomes – but (assuming incomes above a certain minimum threshold) if we all gain an increase in income but remain in the same relative economic position, happiness remains unchanged (Easterlin, 1973; Easterlin, 2001).  Even those who achieve upward mobility might not gain happiness: instead of comparing to a stable reference group, they might look further up the economic scale, raising their economic aspirations and thus remaining dissatisfied (Clark, Frijters and Shields, 2008; Boyce, Brown and Moore, 2010).

These relationships between income and happiness suggest a pessimistic forecast regarding migrants’ experiences. Migrants moving to wealthier countries commonly hold a ‘middling’ economic position in the origin country. (The poorest, especially people in the poorest countries, are often too poor to be able to migrate.) But migrants often find it difficult to gain a job that gives them a ‘middling’ economic position in the destination country; typical obstacles include discrimination, language difficulties, and qualifications that aren’t recognized locally. The lower-level jobs they can get may well offer higher wages in an absolute sense: a cab driver in London probably makes more than a teacher in Romania. But as a matter of relative position, migrants of this type are likely to experience a decrease in their relative position – one that might well translate into a decrease in happiness.

Analysing migrants’ experiences using ESS data

ESS data facilitate an investigation of the effects of migration on subjective happiness via a comparison between the experiences of migrants and stayers in the countries the migrants come from. The ESS is well suited to the study of migrants. The survey samples the adult population (aged 15+ and resident in private households) in each country regardless of citizenship and so includes recent and more established migrants as well as natives of the country. The survey includes a question asking for country of birth (in many surveys, only continent/region of birth is asked of immigrants) so migrants can be matched to stayers in the origin country.¹ Finally, it is possible to pool data across rounds to get sufficient numbers of migrants in destination countries (typically a small part of any national sample) for analysis.²

The main focus of this analysis is on east–west migration, i.e., on the wellbeing of migrants who leave countries in central and eastern Europe (Bulgaria, Czech Republic, Croatia, Estonia, Hungary, Poland, Russia, Slovenia, Slovakia, Turkey, and Ukraine) and move to a wealthier country in western Europe  (Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the UK).

Wellbeing is measured using an item on happiness from the ESS core questionnaire:

C1. Taking all things together, how happy would you say you are?

Findings

Figure 1 offers three comparisons of the happiness of east–west migrants compared with stayers in the origin countries.   In a simple bivariate comparison, migrants are almost a full point happier than stayers (blue bar ), on a scale where respondents rate their happiness from 0 to 10.  In fact, more than half of that difference is due to characteristics that differ between migrants and stayers, e.g. the fact that migrants are generally younger, healthier, and therefore happier than stayers.  Nevertheless, even after controlling for other determinants of happiness, in a conventional cross-sectional regression analysis, migrants overall are almost half a point happier than stayers (red bar).

If we assume that the happiness of stayers tells us what the happiness of the migrants would have been if they had not become migrants, we might then conclude that migrants have gained happiness of almost half a point as a consequence of their migration to a wealthier country.  But we need to consider another possibility: the migrants might already have been happier than the stayers prior to migration for reasons that we cannot observe and control for in our data. The apparent happiness advantage of migrants might represent a pre-existing difference, not a change for the migrants.

To test for that possibility, we need a technique that goes beyond conventional (‘ordinary least squares’ (OLS)) regression models.  Ideally, one would have longitudinal data on migrants before and after migration, to assess change of happiness for migrants directly – but data of that sort are extremely rare. The analysis approach adopted here provides a next-best alternative. The light blue bar in Figure 1 indicates results from a ‘treatment effects’ analysis. This approach looks for correlations between a model of happiness and a model of the migration decision; if there is a significant correlation i.e. if it appears that the choice to migrate was itself associated with individuals’ levels of happiness, then the coefficient indicating the difference in happiness between migrants and stayers is adjusted accordingly to give the ‘true’ effect of the treatment (in this case having migrated to western Europe) net of any pre-existing differences. In this instance, the negative figure given by the green bar indicates that, overall, migrants were indeed happier than stayers even prior to migration – to such an extent that migration appears in fact to lead to a decrease in their happiness rather than an increase.

Analysis of country-level flows

Overall, then, migration from eastern/central to western Europe does not appear to result in increased happiness for the migrants – and it might well result in a decrease.  But one can also address the question at the level of separate migration flows, taking eastern/central European countries in turn.³

As shown in Figure 2, conventional models suggest that, whilst there is no significant difference in happiness between stayers and migrants from Estonia, Poland, Slovakia, and Ukraine after controlling for observed characteristics, migrants do appear to be happier than stayers when originating from Bulgaria, Czech Republic, Croatia, Hungary, Russia, Slovenia and Turkey (red bars). Analysis using the two-stage ‘treatment effects’ technique (green bars), suggest a more pessimistic assessment however (in line with the overall result).

In some countries – where we found no evidence that the decision to migrate was correlated with pre-existing happiness (countries with no green bar in Figure 2) – the best estimate of the effect of migration on happiness is still provided by the conventional regression results (red bars). There remains evidence that migration may have a small positive impact on happiness for migrants from Czech Republic, Croatia, Hungary, Russia and Slovenia.

However, in several countries, where there was evidence that the decision to migrate was correlated with happiness, estimates need to be adjusted for the possible confounding effects of any pre-existing differences.  After adjusting the estimates in these countries accordingly (green bars),  we see that for migrants from Bulgaria, Estonia, Poland, Slovakia, Turkey and Ukraine, migration may in fact  be associated with a decrease in happiness once pre-existing differences in happiness are taken into account.⁴

The treatment regression technique reports a coefficient for the impact of migration adjusted for the correlation between the dependent variable (happiness) and the treatment condition (migration); this correlation is deduced via a correlation (if any) between the error terms in the two stages of the model. If that correlation (λ) is positive, it suggests a positive relationship between migration and happiness – i.e., that happier people were more inclined to migrate. A positive OLS coefficient for the impact of migration on happiness, then, would falsely suggest that migration led to greater happiness; the treatment regression compensates for a pre-existing difference (a positive correlation), adjusting the migration coefficient downwards.

But one accepts that adjustment only if the correlation between the error terms is statistically significant (at some conventional threshold). If the correlation is not significant, then any indication of a pre-existing difference might be a product of sampling error, and so the OLS result (the difference between migrants and stayers net of the associations with other variables) still provides the best indication of the impact of migration on happiness.  Here the treatment results are accepted if λ is significant at p < 0.05.

If migrants to wealthier countries experience a decline in relative socio-economic position and thus perhaps end up less happy, could migration from a wealthier country to a poorer country lead to more favourable outcomes? Separate analysis of migrants from six wealthy northern European countries (Belgium, Britain, Germany, France, Netherlands, and Switzerland) moving to less wealthy Mediterranean countries (Spain, Portugal, Greece and Cyprus) using ESS data pooled across Rounds 1 to 5 indicates that migrants of this sort end up less happy than stayers in the origin countries, similar to the experience of migrants moving to wealthier countries. (Sample sizes for individual country flows are too small to be analysed separately.) Why might this be the case? Migrants moving to poorer countries in Europe compare favourably in socio-economic terms to natives in the destination country but it is not clear that they actually improve their relative socio-economic position over what it was in the origin country. It is also possible that they continue to compare themselves (unfavourably) to stayers in the origin country rather than to natives in the destination country. See Bartram (2015, forthcoming) for further details of this analysis.

Conclusion

Nevertheless, subject to the caveat about unobserved variables, this analysis of ESS data does suggest that migrants moving to wealthier countries in Europe might not gain happiness and perhaps may even end up less happy after migration. That finding need not imply anything in particular about government policies on migration (e.g. admissions decisions); in liberal societies individuals enjoy the freedom to make choices even when the consequences for their wellbeing might be negative. It  does reinforce the notion that migration is not always the best solution to the problems people face where they live – such that migration advocates should recognise the significance of a ‘right to stay’, not just a ‘right to migrate’ (Oberman, 2011).

The assumption that migration is beneficial most of all to the migrant themselves is pervasive in current discourse on migration in Europe, whether from a “pro” or “anti” position (e.g. Dummett, 2001 and Collier, 2014, respectively). At best, however, migration is beneficial to migrants only sometimes. The finding here might even offer a useful point of information for individuals to consider when deciding whether to move to another country, particular if one’s intended destination is a wealthier country: the higher absolute income might not compensate (in one’s subjective wellbeing) for the experience of a lower relative position.

The Influence of the Environment on Subjective Wellbeing

Air quality can be an important contributory factor to individuals’ wellbeing.  Analysis of European Social Survey (ESS) data finds a significant negative relationship between regional concentrations of sulphur dioxide (SO2) in the atmosphere and life satisfaction across Europe.

Introduction

It is widely acknowledged that a person’s surroundings and local environmental conditions can be important determinants of quality of life. The last decade has seen a rapid expansion of research into location-specific factors as determinants of wellbeing. The subjective wellbeing approach has been used to examine the impact on wellbeing of changes in climate (Rehdanz and Maddison, 2005), air pollution (Welsch, 2002; Welsch, 2006), noise (van Praag and Baarsma, 2005), terrorist threats (Frey, Luechinger and Stutzer, 2009) and environmental attitudes (Ferrer-i-Carbonell and Gowdy, 2007).

However, research studying the link between the environment and subjective wellbeing has been limited by the difficulty, first, of obtaining high quality objective environmental data at sub-national level and, second, being able to link this data back to data on individuals’ wellbeing and other characteristics. Obtaining such data cross-nationally is particularly challenging. Studies have therefore been limited either to country-level analysis which masks potentially important variation at regional level or below, or more detailed analysis of a single country which, however, lacks a cross-national perspective.

The innovative ‘HAPPINESS’1 (HAPpiness, Political Institutions, Natural Environment and Space) project funded by the European Science Foundation aims to fill this evidence gap by using Geographic Information Systems (GIS) to link or ‘map’ respondents to the European Social Survey (ESS) to the environmental features of their local area (down to NUTS32 regional level). We focus here on the project’s findings regarding the link between air pollution and wellbeing, reporting on research which provides the first multi-country analysis of the link between air quality and wellbeing using spatially disaggregated data (see Ferreira et al., 2013 for full details). We focus on the air pollutant sulphur dioxide (SO2), an important indicator of industrial and transport activities that has an adverse impact of human health (Folinsbee, 1992) and is found to be present at a regional level (rather than varying even more locally as is the case with some pollutants). Combining data on SO2 levels with information on the wide range of individual and household level data collected by the ESS we can investigate the relative importance of air quality for wellbeing in comparison to socio-economic and demographic variables such as age, gender, income, marital status and employment status.

Using ESS data to study the environment and wellbeing

We pool data across Rounds 1 to 3 of the ESS (2002/2003 to 2006/2007) to generate sufficiently large samples for sub-national analysis. The final dataset contains approximately 75,000 observations from 23 countries³ which participated in at least two of the first three rounds of the ESS. ESS data allows for regional differentiation down to NUTS3 regional unit. The final level of regional aggregation (NUTS1, NUTS2 or NUTS3) used here varies across participating countries depending on the level of spatial disaggregation provided in the ESS.

An item measuring life satisfaction taken from the ESS core questionnaire, and therefore available in every round, is used as a proxy for subjective wellbeing.

B20. All things considered, how satisfied are you with your life as a whole nowadays? Please answer using this card, where 0 means extremely dissatisfied and 10 means extremely satisfied.

Regional data on SO₂ concentrations were collected from a network of monitoring stations across 248 regions in Europe, from 2001 to 2007, using data from a public air quality database system AirBase.⁴ These data on SO₂ levels were then combined with the individual level data provided by the ESS as well as other sources of contextual data available at regional level (see Appendix Table A.1 for further details) to create a unique dataset with which to analyse how SO₂ concentrations – which are linked to regions and not to specific nations – affect subjective wellbeing across Europe.

Findings

Figure 1 shows the average life satisfaction levels across regions within countries covered by the ESS in Rounds 1 to 3 (2002/2003 to 2006/2007). Overall, Europeans report high levels of life satisfaction. The lowest levels of life satisfaction among the countries in the ESS are found in Portugal (5.47) and in eastern European countries (5.51 in Hungary and 5.80 in Slovakia), with the highest levels in Nordic countries (from 7.74 in Norway to 8.49 in Denmark). Importantly for our purposes, Figure 1 shows that there are also notable variations in life satisfaction across regions within countries. For example, average life satisfaction in Italy ranges from 5.57 in Sardinia to 7.80 in Valle d’Aosta. Might differences in environmental factors explain these differences in wellbeing at regional level?

Figure 1:  Self-assessed life satisfaction in Europe 2002-2007

Figure 2 presents average SO₂ concentrations at regional level across Europe in 2006. Similar to regional life satisfaction scores, for air pollution levels we also observe not only cross-national differences but also within-country variation. Thus, for example, Poland had an average SO₂ concentration of 10.60 μg/m3 at national level but ranging between 4.8 μg/m3 and 21.22 μg/m3 across different regions.

If we compare Figures 1 and 2 we see that the countries with the lowest SO₂ concentrations – Norway and Denmark – are also among the countries with the highest life satisfaction. Within countries such as Poland the areas where SO₂ concentrations are highest (showing in red and orange in Figure 2) are also those areas where life satisfaction tends to be lower than average (showing in red and orange in Figure 1). There is a significant negative correlation (ρ=-0.125) between life satisfaction and SO₂ levels at the regional level.

Statistical modelling was used to take account of the possibility that life satisfaction may also be influenced by a range of individual characteristics such as age, gender, income, self-reported health status, marital status and employment status as well as other regional level factors such as GDP and unemployment rates (see Appendix Table A.1 for details of the data used in this analysis). The results of this analysis confirm that the negative association between SO₂ levels and life satisfaction is robust and remains apparent even after taking these other factors into account. An increase in SO₂ concentrations by 1 μg/m3 is associated with a statistically significant reduction in life satisfaction of between 0.016 and 0.030 points on the 11-point life satisfaction scale. This is higher than previous estimates at national level, which range between 0.003 and 0.006 points for the same increase in SO₂ concentrations (Ferreira et al. 2013). The analysis further suggests that SO₂ has an indirect impact on life satisfaction through health, i.e. higher SO2 concentrations are associated with poorer health which in turn is associated with lower life satisfaction. However, much of negative impact of SO₂ on life satisfaction is a direct effect not captured by physical health-related factors. This demonstrates the importance of studying the effect of environmental factors on people’s wider wellbeing.

Conclusion

Air quality and its effect on public health and welfare is an important concern for policymakers. The analysis presented here provides further evidence of the link between air pollution and individuals’ wellbeing, demonstrating a significant negative relationship between concentrations of SO₂ in the atmosphere and life satisfaction across Europe. The particular contribution of this study, linking high quality survey data from the ESS with rich contextual data to carry out detailed regional level analysis across multiple European countries, has been to demonstrate that the association between pollution levels and wellbeing may be even stronger than previous analysis at national level has suggested. This finding confirms the important role that environmental factors may play in influencing subjective wellbeing and the need for policymakers to remain vigilant in monitoring air quality and enforcing standards.

Satisfied with democracy, satisfied with life?

Analysis of the European Social Survey (ESS) module on ‘Europeans’ Understandings and Evaluations of Democracy’ suggests that people are more satisfied with life in countries where the quality of democracy is high and when they believe in the legitimacy of their democratic regime.

Introduction

As early as Athenian times, Aristotle recognized that one of the main virtues of democracy is its capacity to lead a good life and happiness. Even nowadays, with many voices warning about a crisis of democracy in some European countries, most people around the world tend to think that democracy is still preferable to any other political regime and that people live better lives under democratic governments. Despite this optimistic vision, the question of whether democracy does indeed improve subjective wellbeing remains contested and there is evidence pointing in both directions. Some studies suggest that democracy vastly contributes to increasing people’s wellbeing (Owen, Videras and Willemsen, 2008; Orviska, Caplanova and Hudson, 2014); whereas for others, people’s wellbeing is mostly deemed to be independent of how democratic a country is (Stadelmann-Steffen and Vatter, 2012).

The question about the existence of a relationship between democratic performance and subjective wellbeing is not a trivial one. Certainly, democracy seems to be here to stay, and is generally perceived as a quasi-moral good. But, if democracy does not effectively contribute to increasing individual wellbeing, are we perhaps committing to an ill-fitted political regime? On the other hand, if there is a link between democratic performance and wellbeing, efforts to increase the quality of democracy may have a direct impact on citizens’ subjective wellbeing.

Using ESS data to explore the link between democracy and wellbeing

Thanks to Round 6 (2012/2013) of the European Social Survey (ESS) we are now in a better position to answer the question of whether there is a direct link between democracy and wellbeing, at least in the European context. Round 6 of the ESS includes a module on ‘Europeans’ Understandings and Evaluations of Democracy’, which provides for the first time a comprehensive measure of Europeans’ views and evaluations of democracy. These data allow for a more reliable test of the relationship between democratic performance and subjective wellbeing, both at the individual and the country level, than has previously been possible.

In the analysis that follows, which covers all 29 countries included in ESS Round 6,¹ we use data from the democracy module and a question from the ESS core questionnaire which provides a measure of respondents’ self-assessed satisfaction with life (B20) to explore the link between satisfaction with democracy and satisfaction with life.

B20. All things considered, how satisfied are you with your life as a whole nowadays? Please answer using this card, where 0 means extremely dissatisfied and 10 means extremely satisfied.

Democratic performance and subjective wellbeing across European countries

At the country level, subjective wellbeing varies across countries in a similar way to democratic performance. Figure 1 confirms previous findings (Inglehart and Klingemann, 2000) and shows that in countries such as the Nordic countries, where democracy performs better (as measured by the World Bank’s Worldwide Governance Indicators (www.govindicators.org)), levels of satisfaction with life are higher than in countries where democracy performs worse  (such as Russia or Ukraine). The correlation between average satisfaction with life and democratic performance is 0.79.

However, although it appears to support the hypothesis that democracy enhances wellbeing, Figure 1 may in fact depict a spurious relationship between democracy and life satisfaction. Since democratic performance goes hand in hand with economic performance, wellbeing might be related to economic wealth rather than to democratic quality. In addition, we find in Figure 1 countries with very different degrees of democratic quality displaying very similar levels of satisfaction with life (see, for example, Russia and Kosovo as compared to Lithuania and Portugal). In order to ascertain whether there is indeed a link between democratic quality and satisfaction with life, we also need to consider the relationship at the individual level i.e. do people who hold more positive views of their democracy also have higher wellbeing?

Democratic legitimacy and subjective wellbeing among Europeans

The study of citizens’ attitudes to democracy is framed by the concept of democratic support (Easton, 1965; Easton, 1975). Underlying this concept is the idea that a democracy persists (and ultimately performs well), only when it is supported by its citizens. Using Round 6 (2012/2013) of the ESS, we are able to measure one of the main components of democratic support: Europeans’ beliefs of democratic legitimacy – or the evaluation of democratic performance in the light of some set of ideal standards. The higher the congruence between democratic evaluations (what democracy is) and democratic ideals (how democracy should be), the more strongly an individual believes their democratic regime is legitimate.

This concept of democratic legitimacy fits particularly well with the arguments developed in the literature regarding the relationship between democracy and wellbeing. Subjective wellbeing (or the utility an individual derives from something) is composed both of emotions provoked by different aspects of life (how important they are considered to be), and individuals’ judgments regarding how far these same aspects  are realised either in absolute terms or relative to other people (Frey and Stutzer, 2002). As related to politics, the argument is that the more policy outcomes match with an individual’s preferences, the higher their levels of subjective wellbeing will be (Stadelmann-Steffen and Vatter, 2012; Tavits, 2008). It has previously been argued that, in a well-functioning democratic system, politicians are more responsive to citizens’ preferences – and less corrupt – so subjective wellbeing should be higher (Orviska, Caplanova and Hudson, 2014; Tay, Herian and Diener, 2014). Using the ESS we can now test directly whether there is an effect of democracy on wellbeing.  Is greater perceived legitimacy i.e. greater congruence between citizens’ ideals and evaluations of democracy associated with higher life satisfaction?

Considering that democracy is a multidimensional concept, in the analysis that follows we distinguish between: liberal democracy legitimacy beliefs (i.e. beliefs concerning democratic procedures ); social democracy legitimacy beliefs (beliefs concerning social outcomes); and direct democracy legitimacy beliefs (focusing on participatory mechanisms) (see Appendix for details of how these measures have been constructed). This distinction is driven by an analysis of Europeans’ views of democracy, which appear to be ordered within these three democratic dimensions (Kriesi, Willem and Moncagatte, forthcoming). The specific construction of the three legitimacy scales is based on the approach described by Wessels (forthcoming). From the point of view of subjective wellbeing, it is relevant to consider which of these three types of legitimacy – if any – has a stronger impact on citizens’ utility function.

Figure 2 summarises the results of multilevel regression analysis to uncover the relative importance of a range of different individual and country level factors in predicting satisfaction with life (see Appendix Table A.1 for full results). Corresponding with previous findings, female, married, religious, wealthier and healthier people tend to be more satisfied with life than others. Most interesting for this study is the association between legitimacy beliefs and satisfaction with life.  We find a significant positive association between each of the three measures of legitimacy and satisfaction with life. This is the case even after controlling for individual characteristics and the economic performance of the country, supporting the idea that it is not only economics which matters for satisfaction.² The more a system is perceived as legitimate (that is, the more a democracy is perceived as performing according to an individual’s ideals), the higher is subjective wellbeing. It is notable that beliefs about the legitimacy of democratic social outcomes are more strongly associated with satisfaction than attitudes towards democratic procedures or the availability of mechanisms for direct participation. Democracy is important for wellbeing but some aspects of democratic performance appear to matter more than others.

Comparing more and less democratic countries

But, does this positive relationship between democracy and wellbeing hold in all contexts? Previous studies have found that the relationship between democratic performance and wellbeing differs depending on the economic wealth of a country (Veenhoven, 2000).3 We might also expect the quality of democracy, measured objectively, to condition how much perceived democratic legitimacy matters for individuals’ satisfaction with life; the relative importance attached to expectations and the failure of reality to live up to these expectations may vary under different levels of democratic performance.

Figure 3 shows the strength of the relationship between life satisfaction and perceived democratic legitimacy (for each of the liberal, social and direct democracy dimensions) for countries judged as being of low, medium and high democratic quality according to the Worldwide Governance Indicators (see Appendix Table A.2 for full details of this analysis). The lines in the bottom third of Figure 3 show the relationship between legitimacy beliefs and wellbeing in low quality democracies and the lines in the top third  of Figure 3 show the relationship in high quality democracies. Interestingly, the effect of democratic legitimacy beliefs on life satisfaction is stronger the less the objective democratic quality of a country. In countries where democracy performs worst (as measured by Worldwide Governance Indicators), beliefs that the system is nevertheless legitimate clearly make citizens more satisfied with their lives, whereas in countries where democratic performance is better there is a much weaker relationship between legitimacy beliefs and wellbeing. It may be that, in high quality democracies, citizens have become used to the good functioning of democracy and, therefore, their subjective perceptions as to whether democracy is or is not performing quite as well as they would like  play a less significant role in their satisfaction with life.

Conclusion

The analysis presented here suggests that the general optimism about the virtue of democracy is not without grounds. Countries where the quality of democracy is high also tend to have citizens who are more satisfied in general with their lives. Making use of a unique battery of questions exploring individuals’ attitudes to democracy fielded as part of the ESS Round 6 (2012/2013), we have also shown that, within countries, individuals are more satisfied with their own lives if they perceive their democracy to be legitimate i.e. if they believe that democracy in their country is performing according to how they think democracy should be. This finding highlights the importance of democratic legitimacy not only in ensuring the continuation of democratic regimes but also for ensuring the wellbeing of the population. The challenge for governments across Europe, especially but not only those in low quality democracies, is to do everything they can to improve democratic performance in line with citizens’ expectations.

Can we be satisfied with life without being happy? Life satisfaction and emotional experience

In general people who experience more positive emotions also feel more satisfied with life. However, analysis of the European Social Survey (ESS) suggests that the importance of positive emotions varies across countries depending on cultural values.

Introduction

At first glance, it appears self‐evident that positive emotions enhance our life satisfaction, whereas negative emotions bring us down and decrease our satisfaction with life. However, the question remains as to whether we can be satisfied with life without being happy? Moreover, are there cultural differences in the relationships among life satisfaction, positive emotions, and negative emotions? And, if there are, how can these differences be explained? These are the main questions we seek to answer using data from 29 countries which participated in Round 6 (2012/2013) of the European Social Survey (ESS).

Subjective wellbeing and its components

Life satisfaction, positive and negative emotions can be considered the main components of subjective wellbeing defined as “a person’s cognitive and affective evaluations of his or her life“ (Diener, Lucas and Oishi, 2002: p. 63). The judgment of how satisfied people are with their life is based on a comparison with a standard that each individual sets for themselves (Diener et al., 1985). However, subjective wellbeing also has an affective side, which depends on the experience of different emotions, positive as well as negative. The affective component of subjective wellbeing depends primarily on the frequency and not on the intensity of affective experiences (Diener et al., 1991).

There is evidence that people from different cultures assess positive or negative emotions differently in determining how satisfied they are with their lives (Kuppens, Realo and Diener, 2008) and that the strength of the relationship between life satisfaction and affective components of subjective wellbeing depends on many different factors, such as cultural values (Kuppens, Realo and Diener, 2008; Suh et al., 1998) and personality traits (Kööts-Ausmees, Realo and Allik, 2013), among others.

Exploring subjective wellbeing using the ESS

Here we examine how the experience of positive and negative emotions is related to life satisfaction across 29 European nations, using data from Round 6 of the ESS collected in 2012/2013.¹ The ESS is a valuable source of data with which to explore this relationship for two reasons. First, it provides comparable data for nationally representative samples collected to the highest methodological standards across countries. Second, it includes a range of different indicators of subjective wellbeing including measures of life satisfaction and data about the frequency of emotional experience in the last week.

As part of the ESS core questionnaire, respondents were asked to rate how satisfied they were with their life as a whole on a 0-10 scale:

B20. All things considered, how satisfied are you with your life as a whole nowadays? Please answer using this card, where 0 means extremely dissatisfied and 10 means extremely satisfied.

As part of the rotating module on ‘Personal and Social Wellbeing’ fielded in ESS Round 6 (2012/2013) respondents were also asked about their experience of positive and negative emotions. The frequency of four positive and four negative emotions² during the past week was rated on a 1-4 scale, where 1 indicated very low occurrence (“none or almost none of the time”) and 4 indicated very high occurrence (“all or almost all of the time”).

To the extent that the relationship among the components of subjective wellbeing is found to vary across different European countries, we are interested in exploring whether this might be explained by differences in cultural values. We focus here on the survival/self-expression cultural value dimension. This reflects the extent of the shift within societies from an emphasis on existential economic and physical security to the appreciation of individual autonomy, self-expression, and quality of life (Inglehart and Baker, 2000). Based on earlier research (Kuppens, Realo and Diener, 2008; Kööts-Ausmees, Realo and Allik, 2013) we expect that this value dimension will influence how European nations differ in weighing the affective building blocks of what constitutes a good life.

In our analysis we use national mean scores of survival/self-expression based on country-level data from Wave 4 (2000) and Wave 5 (2005) of the World Values Survey (WVS).³ The scores were retrieved from the WVS website (www.worldvaluessurvey.org). Across the ESS Round 6 countries, self-expression was most highly valued in Sweden, whereas survival was most highly valued in Russia (see Appendix Table A.1).

Findings

To assess whether the relationship between life satisfaction and emotional experience is different across 29 European countries and whether the country-level value dimension of survival/self-expression moderates these differences, we conducted a series of multilevel linear modelling analyses to analyse the attitudes of individuals grouped within countries.

As predicted we find that, at the level of individuals, positive emotions are positively associated with life satisfaction, whereas negative emotions are negatively associated with life satisfaction (see Appendix Table A.2, Model 1). As in earlier studies (Kuppens, Realo and Diener, 2008; Kööts-Ausmees, Realo and Allik, 2013; Bastian et al., 2014), positive emotions predict life satisfaction judgments slightly more strongly than negative emotions. Note though that, although our findings clearly show that life satisfaction and emotional experiences are significantly related to each other, we need to be careful about inferring a causal relationship; on the basis of this evidence we cannot know whether the frequency of positive emotions influences life satisfaction judgments or vice versa.

Figures 1 and 2 provide support for our expectation that the contribution of positive and negative emotions to life satisfaction varies significantly across countries.

The role of cultural values

We next examine the relationship between life satisfaction and emotional experiences in the context of country-level cultural values in order to test whether differences in the strength of this relationship between countries may be explained by differences in the extent to which survival versus self–expression is valued. Figure 3 shows how the importance attached to self-expression vs. survival varies across countries. The distribution of cultural values across countries can be compared with the varying strength of the relationship between emotions and life satisfaction as displayed in Figures 1 and 2 above (see also Appendix Table A.1). Statistical modelling shows that, in fact, only the relationship between positive emotions and life satisfaction judgments (and not negative emotions and life satisfaction judgements) is moderated by survival/self-expression values: countries that score higher on survival values generally seem to promote life satisfaction by emphasizing the importance of pleasant experiences (see Appendix Table A.3, Model 2).⁴ In sum, people in countries that emphasize survival values seem to need more positive emotions to be satisfied with their life than people in countries that rank high on self-expression. Lack of negative emotions, however, seems to be an equally necessary prerequisite for being satisfied with your life regardless of whether countries score high or low on survival/self-expression dimension.

The role of socio-economic development

Given that countries that emphasize self-expression values tend to have higher levels of socio-economic development (Inglehart and Welzel, 2005), one might ask whether it is socio-economic development (and not survival/self-expression values) which moderates the relationship between life satisfaction and positive emotions. Additional analyses using the Human Development Index (HDI) as an indicator of countries’ level of socio-economic development shows that the relationship between life satisfaction and positive affect is indeed significantly weaker in countries with a high level of development (see Appendix Table A.3, Model 3).

It is, however, not possible to disentangle the effects of survival/self-expression and human development on the relationship between life satisfaction and positive emotions across the 29 European countries because the two country-level indicators are so highly correlated with one another.⁵ The stronger relationship between positive emotions and life satisfaction in some European countries may be explained either by a greater emphasis on survival over self-expression values in those countries and/or by a higher score on the HDI.

Conclusion

So, can we be satisfied with life without being happy? Our results show that in general, people appear to need positive emotions to feel satisfied with life but the maximization of pleasure and happiness is not equally important in all European countries. Rather it varies along with cultural values and the country’s level of development; people in survival-oriented countries and/or with a lower level of socioeconomic and human development appear to need more positive stimulation and emotions to be satisfied with life (see also Kööts-Ausmees, Realo and Allik, 2013).⁶

How could these findings be interpreted? In survival-oriented countries, where the level of human development is not very high and where people strive for security, life satisfaction is more strongly related to experiencing frequent positive emotions. On the other hand, in highly developed countries where self-expression is often also valued, the overall level of wellbeing of most individuals is already rather high and/or provided in other ways, so that they can be quite satisfied with life even when experiencing fewer pleasant emotions.

Our findings show how emotional aspects of the good life vary across European countries and that this depends at least partly on the values that characterize one’s culture. Although positive emotions might be universally viewed as desirable, there seem to be distinct cultural differences in how relevant such emotional experiences are to life satisfaction. Our findings help to understand that positive emotions matter and they are not just the opposite side of negative emotions. A happy and peaceful person is more likely to think that his or her life is great. However, these positive feelings seem to play a more crucial role in judging one’s life satisfaction in some societies – most notably in those where survival is uncertain – than in others.