Elsevier

Social Science & Medicine

Volume 152, March 2016, Pages 176-185
Social Science & Medicine

The relative importance of health, income and social relations for subjective well-being: An integrative analysis

https://doi.org/10.1016/j.socscimed.2016.01.046Get rights and content

Highlights

  • A composite measure of social relationships explains the largest variations in SWB followed by health.

  • Five alternative measures of HRQoL give fairly consistent predictions of health on SWB.

  • The relative importance of income for SWB is small, but positive and diminishing.

  • Health and income are relatively more important at the lower end of the SWB distribution.

Abstract

There is much evidence that health, income and social relationships are important for our well-being, but little evidence on their relative importance. This study makes an integrative analysis of the relative influence of health related quality of life (HRQoL), household income and social relationships for subjective well-being (SWB), where SWB is measured by the first three of the five items on the satisfaction with life scale (SWLS). In a comprehensive 2012 survey from six countries, seven disease groups and representative healthy samples (N = 7933) reported their health along several measures of HRQoL. A Shapley value decomposition method measures the relative importance of health, income and social relationships, while a quantile regression model tests how the effects of each of the three predictors vary across different points of SWB distributions. Results are compared with the standard regression. The respective marginal contribution of social relationships, health and income to SWB (as a share of goodness-of-fit) is 50.2, 19.3 and 7.3% when EQ-5D-5L is used as a measure of health. These findings are consistent across models based on five alternative measures of HRQoL. The influence of the key determinants varied significantly between low and high levels of the SWB distribution, with health and income having stronger influence among those with relatively lower SWB. Consistent with several studies, income has a significantly positive association with SWB, but with diminishing importance.

Introduction

In recent years, measures of subjective well-being (SWB) has gained importance as an indicator of economic and social progress (Kahneman et al., 2004, Stiglitz et al., 2009). This is largely because, in addition to material thing, human well-being is determined by many aspects of people's life circumstances such as health, social networks, quality of institutions, or leisure activities. As argued by Diener (1984), SWB is best understood as encompassing three separate aspects, such as life satisfaction, positive affect, and the absence of negative affect. Here we consider the satisfaction with life scale (SWLS), which is a widely used measure of SWB (Stiglitz et al., 2009). It involves an evaluative judgement of how one's quality of life is doing in general (Diener et al., 1985), which requires making an effort and remembering past experiences. It is the most stable dimension of SWB over an individual's life course (Diener, 1984) and robust to the effects of social desirability bias and stable across countries (Pacek and Radcliff, 2008).

SWB is also a population outcome measure beyond morbidity, mortality, and economic status that tells how people perceive the circumstances of their life from their own perspective (Diener and Seligman, 2004). A variety of evidence points to a robust correlation between SWB and alternative measures of personal well-being, such as independently ascertained friends' reports and with health and sleep quality (Diener et al., 2006, Kahneman and Krueger, 2006). SWB-measures provide valid and reliable information on how well people - and the wider societies - are doing, thereby assessing quality of life in addition to economic and social indicators (Diener and Suh, 1997). Thus, SWB data can be used to shape and appraise policy.

Several studies have concluded that health is positively associated with subjective well-being (Binder and Coad, 2011, Cubí-Mollá et al., 2014, Deaton, 2008, Graham, 2008, Okun and George, 1984). In a seminal study by Campbell et al. (1976), health was rated by respondents as the most important factor in happiness. The degree of the association between health and SWB varies as a function of whether health is rated by experts or by self-assessment. Objective measures of health, such as a physician's observations and diagnoses, are less correlated with SWB than subjective measures of health, such as a self-report of overall health status (Diener et al., 1999, Larson, 1978, Okun and George, 1984). However, regardless of how health is measured, health and SWB are significantly associated.

Similarly, numerous studies have been conducted on the effect of income on SWB (Diener and Seligman, 2004, Easterlin, 1995, Ferrer-i-Carbonell, 2005, Rojas, 2011), concluding that the relationship is generally positive but diminishing. In his seminal paper, Easterlin (1995) suggested: “raising the incomes of all, does not increase the happiness of all, because the positive effect of higher income on subjective well-being is offset by the negative effect of higher living level norms brought about by the growth in incomes generally” (p. 36). People either adapt to their circumstances (Diener et al., 1999, Menzel et al., 2002), and hence end up no more satisfied than they were before, or they raise their financial aspirations (Easterlin, 1995), which will make them feel less satisfied with their increase in income.

There is growing evidence that social relationships are crucial for people's health and well-being (Binder and Coad, 2011, Diener and Biswas-Diener, 2011, Lin, 1999). Individual-level social capital can be defined as the social skills and networks that enable an individual to access and/or mobilize resources embodied in social structure in purposive actions (Lin, 1999), which, of course enhance individuals' SWB. It has been argued that social relationships have the power to influence identity and recognition that are essential for the maintenance of mental health and entitlement to social resources (Lin, 1999), which in turn are associated with well-being. Furthermore, research in this area suggest that close supportive relationships are considered a necessary condition for SWB (Diener and Biswas-Diener, 2011, Helliwell and Putnam, 2004). Although social context and individual level effects play a role, studies suggest strong and stable effect of social relationships on SWB (Gleibs et al., 2013, Helliwell and Putnam, 2004).

Despite an increasing interest into the partial effects of health, income and social relationships on SWB, empirical studies on the associations between these integrated factors on SWB are sparse. Most studies examined the link between individuals' subjective health ratings and SWB and found this link to be positive and strong (Dolan et al., 2008, Graham, 2008). Few studies extend to more detailed health measures such as provided by using health state utility (HSU) instruments. For example, Graham et al. (2011) conducted a cross-sectional study for a number of Latin American countries, where EQ-5D measure of health problems was related to health satisfaction and life satisfaction. The present study utilizes several measures of health including objective diagnosis indicators in alternative models to test for the stability of results on the relative importance of health on SWB. Moreover, the measure of social relationships used in this paper is unique in that it provides a composite score, which combines the extent and quality of both primary ties (close friends and families) and secondary ties with the public (social inclusion and isolation).

We apply the Shapley value regression based techniques to determine the relative importance of each variable for SWB. While variance decomposition techniques are common in research related to poverty and income inequalities, few applications exist in SWB studies. Graham and Nikolova (2015) discussed the relative importance of objective vs. subjective perceived opportunities for different SWB dimensions using variance decomposition techniques. Sundmacher et al. (2011) applied similar approach to assess the contribution of material, cultural-behavioural, capability and psychosocial factors to variations in health. They both used a variance decomposition technique proposed by Fields (2003) that allows for a negative value, which creates difficulty in interpretation. The Shapley value regression applied in this paper is calculated across all possible combinations of predictors, and is always positive unlike other net effect measures (Conklin et al., 2004).

We used quantile regression model (QRM) to test whether our predictors are more important for individuals with lower SWB than higher SWB. QRM was introduced in a SWB study by Hohl (2009) using the relationship between income and life satisfaction as an example. Binder and Coad (2011) extended this method to a wider investigation of happiness using health, income and social factors. They used an aggregated health measure (self-reported health and objective health) although objective health might be sufficiently captured by subjective health measures. Yuan and Golpelwar (2013) used a similar approach in testing SWB from the perspective of social quality. More recently, Binder and Coad (2015) examined the relationship between unemployment and SWB, and Graham and Nikolova (2015) assessed the capability-SWB relationship using QRM. The current paper further investigates the wider interrelationships by considering several measures of health and using a composite measure of social relationships.

Based on a comprehensive cross-sectional data set (N = 7933) from six developed countries that combine a healthy group and seven disease groups, this paper aims to answer the following two questions: i) What is the relative importance of health, income and social relationships for SWB?, and; ii) Will the (relative) importance of these three key predictors differ depending on the level of the SWB distributions?

Section snippets

Data

Data was obtained from the multi-instrument comparison (MIC) study, which is based on a 2012 online survey carried out in Australia, Canada, Germany, Norway, UK and the US by a global panel company, CINT Pty Ltd (Richardson et al., 2012). The data include a representative ‘healthy group’ (N = 1760) and seven major disease groups (N = 6173), which give a total sample size of 7933. The survey was approved by the Monash University Human Research Ethics Committee (MUHREC), Melbourne, Australia,

Results

Table 3 reports the regression results of unstandardized and standardized coefficients for each of the three HRQoL-measures. Results from OLS1 includes measures for the three key variables (HRQoL, income, social relationships), but adjusting for variables that are standard to include in this literature: gender, age, unemployment, education, marital status. Results from OLS2 in addition includes dummies for diagnosis and countries. The larger the difference across the three HRQoL-measures after

Discussion

This study examines the relative importance of health, income and social relationship as determinants of SWB. The standardized coefficients and variance decomposition results suggest that measure of health, particularly self-rated measure, have the strongest associations with SWB. For instance, the Shapley value decomposition reveal that the proportion of variation in SWB associated uniquely with VAS is 15.8% after controlling for all other variables including disease and country dummies.

Conclusions

Subjective well-being (SWB) is more than having a good financial standing and the absence of disease. It is an asset that allows people to realize their aspirations, and enhance their social ties. This study provides empirical evidence that health, income and social relationships are positively associated with SWB even after controlling for individual, household and national-level control variables. The study reveals that the aggregate measure of social relationship is the most important

Acknowledgements

We are grateful to John Brazier and two anonymous referees for their very constructive comments. Data collection was funded by grants from The Australian National Health and Medical Research Council (grant number 1006334), while the Norwegian arm was funded by the University of Tromsø. The Research Council of Norway, grant number 221452, funded the preparation of this manuscript.

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