Quality of Life, Well-Being and Social Policies in European Countries1

Abstract

European Public Policies have traditionally focused on material welfare conditions and indicators, but recent studies demand the inclusion of other subjective indicators. This work deals with the need to go beyond welfare to well‐being and aims a critical review of the scientific literature on subjective well‐being and quality of life in social policies, and of the indicators usually managed for its operationalization. A comparative study of different variables used by the OECD Better Life Index (BLI, 2014) has been carried out to analyze the relationship between social and economic indicators and the other indicators traditionally linked to life satisfaction and subjective perception of life satisfaction. As the main result, this research remarks the need to include social policies in analyses of well‐being as a key element in people's satisfaction, recognizing the perception of subjective well‐being and quality of life as a political and public issue.

1. Introduction

There is today considerable discussion on the indicators that best measure subjective well‐being in relation to the public policies enacted. Most of the proposals are based on an economic point of view [14, 18, 24] and, so, social development has usually been measured by traditional indicators such as GDP, gross revenue, employment and unemployment, poverty, and social exclusion rates. However, in order to observe well‐being and economic and social development, recent studies point to the relevance of including indicators related to personal satisfaction and social policies [2, 16] and, also, to the inclusion of subjective indicators to cater for aspects traditionally relegated to families’ private lives, such as care or the perception of life satisfaction [4, 7, 9, 17]. Therefore, public policies implemented in each country would play an essential role in the quality of life of its citizens but, even though numerous indicators are being tried to measure people's quality of life and well‐being, they usually do not include social policies and the need for care [3, 13].

While the traditional idea of material welfare would seem to be displaced by the concept of subjective well‐being [11], sociology and economic science still operationalize well‐being by means of indicators, such as employment, income, housing, health, and so on [13]. In the last decade, numerous studies have been published highlighting the importance of non‐economic indicators for personal satisfaction, including those related to social policies that favor work and family compatibility [21], but we do not have sufficient data and empirical analysis in this respect. Only recently some studies have focused on indicators like personal satisfaction with one's job, the family, the neighborhood, the environment, and so on. As an example, we can point to the 2013 OECD Family Database, which measures family policies by means of a wide range of indicators (direct social spending on families, services to attend to dependents, parental leave, childcare, working hours, etc.). However, none of these definitions explicitly relate family policies with people's quality of life.

The difficulties in operationalizing well‐being reveal that its definition is a complex task in itself. Griffin [8] links the definition of well‐being to the way and degree of satisfying basic needs, whereas Zimmerman [27] states that well‐being, in terms of quality of life, can be conceived in very different forms, depending on the country or region studied, and Sen [23] points that well‐being should be interpreted considering how a person “functions in the broadest sense.” Moreover, Böhnke [1] and Watson et al. [26] consider that the evaluation of quality of life should not be defined merely by means of economic and material criteria [24], but that the way in which social policies and institutions contribute to well‐being also needs to be analyzed.

Cross‐national studies comparing and analyzing variations in quality of life across different countries reveal that well‐being is influenced not only by economic factors but also by other elements, such as social policies, health, or confidence [1, 6, 26]. In addition, the emerging research on subjective well‐being shows that social and family policies implemented in different countries would help self‐perception of happiness by means of work and family balance, minimization of conflicts between work and family life and, in consequence, increasing parents’ satisfaction [13, 19, 22, 25]. Along this line, Wallace and Abbot [25] show the relation between the development of family policies and the well‐being of parents regarding employment and family, as well as the variations between countries [13]. According to this perspective, well‐being should be measured by means of subjective quality of life indicators referring to how individuals feel, how they perceive happiness, and so forth [5].

Based on the aforementioned research, the purpose of this work is to analyze the relationship between economic and social indicators and life satisfaction, in order to contribute to the ongoing discussion on subjective well‐being, social policies, and economic development in the modern Welfare States in Europe. For this purpose, several indicators are analyzed in relation to people's satisfaction, in order to observe the influence of social and economic matters in well‐being and quality of life in European countries.

2. Method

Interest in quality of life has increased in recent decades in Europe, as evidenced by the three European Quality of Life Surveys (2003; 2007; 2012) published by Eurofound and the well‐being modules incorporated into the European Social Survey. This work analyzes indicators relative to family policies, well‐being, and quality of life from the 2013 OECD family policy database and the well‐being module in the 2010 European Social Survey. The indicators selected for this study have been validated and applied in previous research [13].

The data analysis carried out based on bivariate analyses has also been made of correlations to determine whether there is any type of association between the measure of well‐being and other indicators traditionally linked to life satisfaction and subjective perception of happiness (including work and family balance). For this purpose, we have considered the indicators included in the OECD Better Life Index (BLI) for 2014. This index incorporates different dimensions of well‐being: income and wealth, jobs and earnings, housing, health status, work and life, education and skills, social connections/community, civic engagement and governance, environmental quality, personal security/safety, and, finally, life satisfaction (subjective well‐being). The countries used for the analysis are those for which comparable national data were available: Denmark, Sweden, the United Kingdom, France, Finland, Netherlands, Spain, Slovenia, Germany, Ireland, Portugal, Greece, and Belgium.

The analysis tries to illustrate the correlation between different aspects of well‐being in various European countries. Although the statistical technique applied does not permit direct causality relationships to be established, it does at least allow the identification of descriptive patterns to highlight the possible associations existing between indicators of different dimensions of well‐being measured through life satisfaction. This analysis can therefore serve as a reference or inspiration for future works of research on this subject.

To refer to quality of life and well‐being, we selected the following indicators included in the OECD Better Life Index (BLI) for 2014:

• Income and wealth

  1. Household net adjusted disposable income

  2. Household net financial wealth

• Jobs and earnings

  1. Employment rate

  2. Long‐term unemployment rate

  3. Average gross annual earnings of full‐time employees/personal earnings

  4. Job/employment insecurity

• Housing

  1. Number of rooms per person/rooms per person

  2. Dwellings without basic facilities

  3. Housing expenditure

• Health status

  1. Life expectancy at birth

  2. Self‐reported health status

• Work and family life balance

  1. Employees working very long hours

  2. Time devoted to leisure and personal care

• Education and skills

  1. Educational attainment

  2. Students’ cognitive skills

  3. Expected years in education

• Social connections

  1. Social network support

• Civic engagement and governance

  1. Consultation on rule‐making

  2. Voter turnout

• Environmental quality

  1. Air pollution

  2. Satisfaction with water quality

• Personal security/safety

  1. Homicides rates

  2. Self‐reported victimization/assault rate

• Life satisfaction

  1. Subjective well‐being

The source used is the OECD Better Life Index (BLI) for 2014. As it is described at the OECD website (www.oecd.org), the OECD Better Life Initiative, launched in 2011, focuses on the aspect s of life that matter to people and that shape their quality of life. The Initiative comprises a set of regularly updated well‐being indicators and an analysis, published in the How’s Life? Report and available in the Better Life Index interactive web application. It also includes several methodological and research projects to improve the information base toward a better understanding of well‐being trends and their drivers.

3. Data analysis

Numerous reports have highlighted the fact that the current economic recession has accentuated inequality in Europe. This is due, among other reasons, to the effects of cutbacks in public social policies and the effects of unemployment [14, 15], but if we observe the case of different countries across Europe it is possible to appreciate a very diverse impact. Besides the characterization of the different welfare states, social theory has not been able to offer a holistic explanation about the diversity observed in the family policies across Europe [13]. An interdisciplinary perspective would help us to understand how social groups have collaborated, interacting with their natural, social, and cultural environment, to achieve greater or lesser confidence in public sphere. This interaction would have favored governments to promote different models of social policies and well‐being [12].

To examine which variables are more connected to the OECD's measure of subjective well‐being, we will first analyze the correlations between the life satisfaction score and the other indicators. For this purpose, we will consider the variables included in the OECD Better Life Index (BLI) for 2014. This index incorporates different dimensions of well‐being: income and wealth, jobs and earnings, housing, health status, work and life, education and skills, social connections/community, civic engagement and governance, environmental quality, personal security/safety, and, finally, life satisfaction (subjective well‐being). From the variables included in the model, we have selected those that showed a significant correlation with each other.

First, if we pay some attention to the complete list of dimensions of well‐being and indicators that include the OECD Better Life Index (2014) (Table 1), the lack of variables measuring public policies’ support to citizens (by means of social policies, public services, family policies, etc.) is remarkable. It is also important to remark the minority presence of indicators linked to family and social network supports (only one variable refers to this) and to care (care policies are not included in the list, and personal care is only present in the work and life balance dimension of well‐being).

Variable 1		Variable 2 Life satisfaction (subjective well‐being)
		R	R^2
Income and wealth	Household net adjusted disposable income	0.787300177	0.619841569
	Household net financial wealth	0.586238933	0.343675805
Jobs and earnings	Employment rate	0.785112796	0.616402102
	Long‐term unemployment rate	−0.813668032	0.662055667
	Average gross annual earnings of full‐time employees	0.82119741	0.674365136
	Job/employment insecurity	−0.565872766	0.320211387
Housing	Number of rooms per person/rooms per person	0.742187265	0.550841337
	Dwellings without basic facilities	−0.069756495	0.004865369
	Housing expenditure	−0.099331557	0.009866758
Health status	Life expectancy at birth	−0.00977963	9.56412E−05
	Self‐reported health status	0.391878808	0.153569
Work and life balance	Employees working very long hours	−0.466903475	0.217998855
	Time devoted to leisure and personal care	0.368469332	0.135769648
Education and skills	Educational attainment	0.615230547	0.378508626
	Students cognitive Skills	0.667809325	0.445969294
	Expected years in education	0.266057546	0.070786618
Social connections	Social network support	0.789315912	0.623019609
Civic engagement	Consultation on rule‐making	0.089489741	0.008008414
	Voter turnout	0.724350929	0.524684269
Environmental quality	Air pollution	−0.421266448	0.17746542
	Satisfaction with water quality	0.757415786	0.573678673
Personal security/safety	Homicides rates	−0.01851255	0.000342715
	Self‐reported victimization/assault rate	−0.093732063	0.0087857

Table 1.

Pearson correlation test (R) and determination test (R²). OECD BLI (2014).

Source: own elaboration from the OECD BLI (2014).

In addition, Table 1 shows that most of the significant correlations refers to indicators linked to material conditions of life. This is the case of jobs and earnings, and income and wealth dimensions of well‐being, where we can find variables with significant correlations with life satisfaction such as personal earnings (R = 0.82119741), long‐term unemployment rate (R = −0.813668032), employment rate (R = 0.785112796), or household net adjusted disposable income (R = 0.787300177). We will focus on the analysis of these indicators, and also on social network support (R = 0.789315912) because these are the top five variables with more significant correlations with life satisfaction (subjective well‐being) scores.

The combined analysis of personal earnings and subjective well‐being (Figure 1) evidences that life satisfaction score is higher in countries with high personal earnings. The cases of Scandinavian countries (Sweden, Finland, Denmark) and the Netherlands are especially notable, with life satisfaction scores higher than expected according to the trend line. The same analysis can be applied to household net adjusted disposable income (Figure 2), an indicator that also gives higher results for life satisfaction than expected. Again, with personal earnings, Ireland would be a relative exception, with a higher level of personal earnings but with life satisfaction score lower than the expected trend. We can find a possible explanation for this attending to other indicators of the job and earnings dimension of well‐being: employment and unemployment rates are worse for Ireland than for other countries with high personal earnings, and this can be analyzed as an unequal distribution of wealth, that may be linked to a lack of strong social policies and welfare state that we can find in the Scandinavian countries. Thereby, these data not only show that personal earnings are clearly considered in the OECD's measures of subjective well‐being, but also remark that other social elements, such as public policies, should have a heavier presence to correct cases of unequal distribution of wealth.

Figure 1.

Life satisfaction (subjective well‐being) score and personal earnings (USD/year). Source: Own elaboration from OECD BLI 2014.

Figure 2.

Life satisfaction (subjective well‐being) score and household disposable income (USD at current PPPs per capita). Source: Own elaboration from OECD BLI 2014.

Employment (Figure 3) and long‐term unemployment (Figure 4) are other material indicators that have a very important weight in the measures of subjective well‐being. So, again countries with high scores for life satisfaction are also countries with favorable employment and long‐term unemployment rates. Denmark, Finland, and the Netherlands are fresh countries with higher scores for life satisfaction than expected according to the respective trend lines. On the other hand, Greece and Portugal are countries with lower life satisfaction scores, have unfavorable rates for employment and long‐term unemployment, and also are under the respective expected trend line. Ireland and Spain are the exception cases, as they have better scores for subjective well‐being than expected for their employment and long‐term unemployment rates, but this could be explained by other factors, such as social and familial network support (as we analyze below).

Figure 3.

Life satisfaction (subjective well‐being) score and employment rate (%). Source: Own elaboration from OECD BLI 2014.

Figure 4.

Life satisfaction (subjective well‐being) score and long‐term unemployment rate (%). Source: Own elaboration from OECD BLI 2014.

Social network support (Figure 5) also shows significant correlation with subjective well‐being, but in this case, it cannot be considered as a neat material indicator. In relation to the Scandinavian countries, they present again for this variable life satisfaction scores over the expected trend, just in the same way that countries such as Greece and Portugal have low rates and scores. As we said before, it is remarkable that the cases of Ireland and Spain, with relative mid‐high rates for social network support (similar, or even higher in the case of Ireland, Finland, to Sweden, and the Netherlands scores) that do not correspond with the expected life satisfaction rates, allow us to state this social indicator as a factor that probably would correct the previously mentioned mismatch between subjective well‐being and general employment rates.

Figure 5.

Life satisfaction (subjective well‐being) score and social network support rate (%). Source: own elaboration from OECD BLI 2014.

Social network support would be, therefore, the only social and non‐material indicator with significant correlations when measuring subjective well‐being. On the contrary, we should remark a lack of significant correlation between subjective well‐being and the only OECD BLI variable that includes care components: time devoted to leisure and personal care (see Figure 6). This data reveals a possible secondary relevance of care attitudes and policies when measuring life satisfaction; especially in extreme examples such as Spain, a country despite having higher data (together with Denmark) presents, on the other hand, a relatively low score on subjective well‐being, or on the contrary, Finland and the United Kingdom, with two of the lower scores on time devoted to leisure time and care, but with a life satisfaction punctuation higher than statistically expected. A possible explanation for the absence of such a correlation may be found considering that care and social policies still belong to private and family spheres, and they do not become visible as a factor of well‐being despite its great importance to personal subjective well‐being. So, northern European countries, historically with stronger welfare state policies, present higher correlation index than southern and Mediterranean countries, such as Spain, Greece, and Portugal, where these public services have been defamiliarized long ago.

Figure 6.

Life satisfaction (subjective well‐being) score and time devoted to leisure and personal care (hours/day). Source: Own elaboration from OECD BLI 2014.

The results lead us to support that studies and surveys on well‐being should consider indicators not only related to economic situation but also personal satisfaction with life and the public policies developed. To improve the comprehension of quality of life and also to mobilize public resources and public action in favor of greater individual and familial well‐being, it would be useful to introduce indicators that are able to measure the development of social services and social and family policies [13].

4. Conclusions

Following our data analysis, our proposals for future research of well‐being would point to a critical review in the operationalization of several social indicators. We detect a need to go more deeply into the operationalization of social indicators, as a more subtle and exact form of well‐being in terms of welfare, so further research would need to go beyond traditional economic variables in order to observe self‐perception of well‐being. A methodological alternative would be to combine the results obtained by regular macro‐surveys with more comprehensive studies, from a qualitative viewpoint [10, 20, 21], in order to incorporate information which would lead to a more comprehensive understanding of subjective well‐being.

In this respect, we must insist on the relevance of a more detailed analysis of the relationship between life satisfaction geared to services and individual well‐being, to determine whether states which promote service‐oriented public spending achieve greater levels of individual well‐being. Results point to the challenge of explaining the relationship between life satisfaction and well‐being in different European countries. We can state that the observed differences need to be explained according to the role played by social policies in every cultural context. This would have helped the regulatory change in some countries regarding the development of social policies, in benefit of the entire community by means of greater long‐term welfare of citizens [13].

The limitations of this analysis are obvious, since well‐being theory has had little development and applicability in social and political studies. However, the contributions presented in this work show the need to move forward in the uses of well‐being perspective in social and political studies, by including social indicators in analyses of well‐being, as a key element in people's satisfaction. This study points to a possible association between the development of public care policies and citizens’ satisfaction, because in northern European countries there is great development of social policies. This could be interpreted as the development of social policies in these countries because of the need for care associated to the new family lifestyles. The new theoretical insight suggests that the advances made in European citizens’ quality of life and well‐being are the result of development of social policies as a means of optimizing the management and political organization of welfare. Therefore, the strength of the social approach through well‐being perspective can help us to understand how social policies will vary in different economic and social contexts.

In short, from the data and information provided and analyzed earlier, we can state that the OECD measures for well‐being clearly privilege material conditions of life, traditionally linked to welfare, rather than subjective conditions for happiness, life satisfaction, and subjective well‐being. Also, neither public services nor political support (by means of social policies) are considered for the evaluation of life satisfaction. This clearly indicates that well‐being operationalization barely considers political support and subjective conditions. In order to test this affirmation, future research should include the analysis of correlations between OECD subjective well‐being and data for indicators measuring the influence of public policies and services on inhabitants’ quality of life. The results obtained in this analysis emphasize the relevance of social factors, such as labor, family, and social relations compatibility, to explain personal satisfaction and subjective well‐being, as well as its variability among the different European countries, beyond the most usual economic factors. This finding is of great relevance for the design of social and economic policies in different welfare states. In short, these results allow us to conclude that the progress and development of a country depends not only on the evolution of the macroeconomic indicators but also on the social policies developed by different States and their impact on the personal satisfaction and well‐being of the citizens.