Are Happiness and Unhappiness Two Sides of the Same Coin?

Transcription

1 Special Issue Article Japanese Association for Mathematical Sociology Are Happiness and Unhappiness Two Sides of the Same Coin? An Analysis of Happiness and Unhappiness Jun Kobayashi (Seikei University) Carola Hommerich (Hokkaido University) Abstract Within the booming field of research on subjective well-being, happiness and unhappiness have so far been treated as two ends of a continuum with causes and mechanisms being the same for both. Still, this is not self-evident. We here use the SSP2015 survey data to investigate whether happiness and unhappiness have the same determinants. To do so, we classify the respondents into three well-being groups: the happier than average, the average, and the less happy than average. We conduct a multinomial logistic regression analysis to disentangle the effects of education depending on the level of happiness. Our results imply that (1) more education promotes happiness of unhappy people. At the same time, however, we find that (2) an increase in education reduces the happiness of happy people. This means that the impact of education on happiness is by no means straightforward, but that it can have opposing effects depending on the happiness level. This supports our hypothesis that some determinants have different effects on different happiness levels. It also implies that an enhancement of subjective well-being cannot be achieved in the same way for happy and unhappy people. Therefore, happiness and unhappiness turn out not to be two sides of the same coin. Keywords happiness, unhappiness, subjective well-being, education, multinomial logistic regression analysis Review History Received March 8, 2017/Accepted May 10, Introduction All happy families are alike, each unhappy family is unhappy in its own way (Anna Karenina by Leo Tolstoy). The question of how to achieve a happy life is as old as mankind. Already Aristotle argued that 49

2 people should pursue happiness because it is the highest virtue we can achieve in our lives (Aristotle 2014). Ideas about how this can be done, however, have changed throughout the centuries. Especially since the advent of modernity, it was common sense that economic growth is the key to happiness. But in recent years, the post-modern realization that economic affluence alone is not enough to guarantee happiness has triggered a new interest in how happiness can be achieved. Happiness studies have become an established field in the social sciences, which has generated much scholarly output over the past four decades (for a review of the most relevant literature see Dolan et al and Diener et al. 1999). While there are various measurements of happiness, they all share the assumption that happiness and unhappiness are two ends of a single continuum on a single dimension without discontinuity. That is, if a person is not unhappy, it automatically implies that he or she will be happy. And although happiness researchers usually admit that happiness means different things for different people (Frey and Stutzer 2002: 3), it is generally assumed that determinants of happiness and unhappiness work in the same way on this continuum, just like two sides of the same coin. However, as Leo Tolstoy hints in his famous opening sentence to Anna Karenina, it can be imagined that causes and mechanisms are not the same. Rather, as happiness is a rich and by no means straightforward concept, it may comprise of multi-dimensional, multi-layered mechanisms rather than a single one that applies universally. This means that different determinants could function in different ways for certain groups, as has, for example, been shown when comparing groups with different employment status (Tiefenbach and Kohlbacher 2015b) or age groups (Hommerich 2017). Similarly, what causes an increase or decrease of subjective well-being could differ according to whether a person was happy or unhappy. 1) What might sound like a tautology at first is actually a sensible question to ask. Evaluations of individual happiness are not made in a vacuum, but are conducted within the context of the present state of mind, or, to say it differently, within the context of the individual level of happiness at that point in time. With this assumption in mind, several questions arise. Is happiness determined in the same way for the happy and the unhappy? Do determinants have differing effects depending on whether a person is happy or unhappy? Is unhappiness possibly determined in more diverse ways than happiness, as suggested by Tolstoy? In this paper, to tackle these questions, we focus on the following research question. Research question. Are determinants of happiness the same for happy and unhappy people? An answer to this question would not only further deepen our understanding of what shapes individual happiness, but would also help us better target policy measures that aim at an increase in quality of life. 50

3 Are Happiness and Unhappiness Two Sides of the Same Coin? 2 Literature and Hypotheses 2.1 Literature Through the recent boom of so-called happiness studies in economics, psychology, and sociology, the question of what determines happiness has received much scholarship and the existing body of literature is vast. In pursuit of an overview, Layard (2005) summarizes the main seven determinants of happiness as follows: family, income, employment, community and friends, health, personal freedom, and philosophy of life in this order of their impacts on subjective well-being with family being of the highest importance. We base our own analysis on his classification. However, we additionally include education, which we believe to be of special importance in the case of Japan (This will be explained in more detail below). This variable has been somewhat disregarded so far, as its relationship with happiness cannot be grasped easily and results of previous research are mixed. When thinking about happiness as a multidimensional concept, however, it might be possible to better understand its effect on subjective well-being. For our literature review, we only review previous research for the variables that are relevant to our own analysis (for a Japan-focused in-depth overview, see Tiefenbach and Kohlbacher 2015a). Regarding demographics, females tend to be happier than males (Bonke et al. 2009, Graham and Chattopadhyay 2012), with this gap being especially large in Japan (see Kusago 2008 amongst others). Age tends to have a U-shaped curve over the life cycle, with lowest levels of live satisfaction in their late 40s (Blanchflower and Oswald 2007). Married people are usually happier than singles (Tsang et al. 2003). While health mostly promotes happiness, sickness does not necessarily decrease happiness (Diener and Seligman 2004). A positive effect for subjective well-being has also been reported for resources of social capital (see Hommerich and Tiefenbach 2017 for an overview). Richer social networks (Diener and Seligman 2002), having many friends (Hintikka et al. 2000), and volunteering (Thoits and Hewitt 2001) all show positive relationships with happiness. With regard to socio-economic status, analyses have focused on the relationships of happiness with income and employment relations. While the well-known Easterlin paradox (Easterlin 1974) might suggest otherwise, the majority of the international literature shows a positive correlation between income and employment with happiness (for instance, Clark et al for an overview). Debate remains whether this relationship is relative or absolute. With regard to the latter, it seems that the positive effect on happiness of an increase in absolute income is smaller for higher income levels, at least in the United States (Diener et al. 1993). A clear reducing effect on happiness has been reported for unemployment, both in the international literature and in Japan-related studies (Di Tella et al. 2001; Frey and Stutzer 2002; Ohtake 2012, Winkelmann and Winkelmann 1998). As mentioned above, effects of education are mixed. Some studies find a positive relationship with subjective well-being (for instance, Blanchflower and Oswald 2004), while others report peak levels of life satisfaction for middle levels of education (Stutzer 2004), indicating a nonlinear effect. What makes an investigation of the relationship of education and happiness especially complex are its indirect effects: Education has a strong positive relationship with employment status and income as well as health, and thereby impacts subjective well-being (also) indirectly. What is more, education raises people s opportunities and aspirations. This can be positive when expectations are met. However, it can also create pressure of having to achieve more than 51

4 possessed at present, which eventually generates a negative effect on happiness. Ferrante (2009) finds for Italy, for example, that as educational levels rise above secondary schooling, perceived opportunities start to exert a negative effect on life satisfaction. 2.2 Theory All studies reviewed here assume happiness itself to be one-dimensional with effects being the same for unhappy and happy people. In this paper, we take a different view: By treating happy and unhappy people as separate groups, we test possible different ways of how subjective well-being is determined depending on whether an individual is happy or unhappy. In present-day Japan, there still is a strong belief in the promise of meritocracy as the facilitator of upward mobility. While a university education might have guaranteed a stable, high status job up until the late 1980s, it can be contested whether this still holds true today, as shares of atypical employment are increasing and careers are less stable than they used to be (Chiavacci and Hommerich 2017). As a result, an individual s job has become a less significant determinant of social consciousness and personal identity. At the same time, the role of education has changed, as educational credentials have become more than just a means for obtaining occupational status (Kikkawa 2016). According to Kikkawa s interpretation of Gidden s theory of reflexive modernity, education has become a symbolic token indicative of their (individuals ) social status and life opportunities (Kikkawa 2016: 167). As careers have become more fluid and individuals have to rewrite their résumés countless times in their lives (Kikkawa 2016), education remains important in shaping social status throughout the life course and becomes the basis for an ongoing process of reflexive self-evaluation. Within the context of subjective well-being, we believe that this symbolic value of educational credentials should have a special impact on personal happiness in contemporary Japan. This impact may differ depending on whether an individual evaluates his or her life as favorable ( happy ) or unfavorable ( unhappy ). 2.3 Hypotheses Based on the above, we formulate the following general hypothesis to be tested: Hypothesis 1. Determinants of happiness differ depending on whether a person evaluates him- or herself as happy or unhappy. Given the symbolic value of education for self-evaluation discussed above, we expect to find a difference in its impact on happiness between people who evaluate their life favorably or unfavorably. Assuming that education is interpreted as the means to achieve or maintain a certain social status, then we expect its impact on self-evaluation to differ between people who are happy in their present life situation and those who are not. For someone who thinks of him- or herself as unhappy, an increase in education might bring the (even if only symbolic) increase in life chances that are indispensable to achieve the living situation the individual is striving for. Thus, this may result in an increase of his or her happiness. For an individual who evaluates his or her life favorably ( happy ), an increase in education is not necessarily indispensable and will have less impact on his or her happiness (Figure 1). We thus 52

5 Are Happiness and Unhappiness Two Sides of the Same Coin? Figure 1. Hypothesis 2 (Note) Arrows stand for causal relationships. Figure 2. Distribution of happiness (Note) Total N = 2,746. Mean is 6.8, and median is 7. hypothesize: Hypothesis 2. For unhappy people, an increase in education will have a large positive effect on happiness. For happy people, this effect will also be positive, but smaller. 3 Methodology and variable selection 3.1 Data For our analysis, we use data of the Stratification and Social Psychology Survey 2015 (SSP2015). The data were collected in computer assisted personal interviews (CAPI) from January to June Three-stage stratified random sampling was used to draw a national representative sample from Japanese males and females of age 20 to 64 (as of December 31, 2014), resulting in 3,575 valid responses (response rate = 43.0%). In this paper, we use a subsample of 2,746 respondents who answered all questions relevant to the subsequent analyses. On average, the respondents in our subsample are 45.5 years old, have 13.4 years of education, and an equivalent household income of 3.8 million yens. Forty-seven percent of them are males, 20.3% were never married, 72.1% were married, and 7.6% were separated or divorced. 53

6 3.2 Dependent variable: happiness Our dependent variable is the overall happiness of the respondents. We rely on the following standard single-item measure of happiness, asking respondents to rank their happiness on an 11-point scale from 0 = very unhappy to 10 = very happy: Question (happiness). How happy are you at present? Very unhappy Very happy Figure 2 shows its distribution. The mean was 6.8 (median = 7, standard deviation = 1.9). 3.3 Independent and control variables As controls, we include a number of variables linked with happiness in the literature discussed above: gender (male = 1, female = 0), age, equivalent household income, employment status (employed = 1, otherwise = 0), years of residence (to measure social networks within the local community), number of friends ( How many people do you rely on and are friendly with on a daily basis? ), subjectively rated health (measured on a five-point scale from bad = 1 to good = 5), subjectively rated freedom ( I can mainly decide the way I live my life based on my own ideas on a five-point scale from does not apply = 1 to applies = 5). These items cover six out of the seven main determinants of happiness identified by Layard (2005). 2) To measure our main variable of interest, education, we include years of education as the independent variable. 3.4 Analytical strategy To test our hypotheses, we choose a two-step approach. First, we carry out an OLS-regression to test the relationship between our independent and control variables and happiness levels for the overall sample. Then, we classify our data into three subsamples and run a multinomial logistic regression. This is to calculate the chances of unhappy respondents to become happier (to achieve at least average happiness), and of happy respondents to remain happy (to remain at least happier than average). As the analysis is based on cross-sectional data, it is important to keep in mind that the variables do not have a temporal order. Our assumptions on a causal order of variables are developed from theory. 4 Results 4.1 Descriptive results Table 1 displays the mean happiness levels for our independent and control variables, with most differences being significant as expected based on the literature discussed above. Females and 54

7 Are Happiness and Unhappiness Two Sides of the Same Coin? Table 1. Mean happiness by demographics and socio-economic variables Frequency Mean happiness ANOVA Total 2, Sex Female 1, *** Male 1, Age 20s *** 30s s s s Marital status Never married *** Married 1, Separated, divorced Number of friends *** 3-1, Years of residence *** Health (1-5) 1-3 1, *** 4-5 1, Freedom (1-5) 1-3 1, *** 4-5 1, Employment Unemployed Employed 2, Equivalent income *** 200-1, Education Junior high school *** High school 1, College 1, (Note) Total N = 2,746. Income is in ten thousand yens. p<.10, *.5, **.01, ***.001. married respondents are more likely to feel happy. While the results for age do not show a linear trend, happiness levels appear to be higher among the younger age groups. If respondents have more friends, they have higher chances of being happy. Respondents who had lived more years in their current community were less likely to be happy a result which is rather counter-intuitive. With an increase in subjective health, freedom, income, and education, happiness levels rise. However, unlike the literature, respondents did not differ in their happiness levels depending on whether or not they were employed. 4.2 Grouping by levels of happiness For the multinomial logistic regression, we classify our respondents into three groups according to their levels of happiness. As reported above, the mean level of happiness was 6.8, with the median at 7. Therefore, we use point 7 as the average happiness level, and thus we group respondents 55

8 Table 2. Descriptive statistics of three happiness groups Group N Male Age Married Number of friends Years of residence Health Freedom Employed Equivalent income Years of education Unhappy 1, % % % Average % % % Happy 1, % % % Test *** ** *** *** *** *** *** ** *** (Note) Total N = 2,746. Income is in ten thousand yens. p<.10, *.5, **.01, ***.001 in chi-squared tests for percentages and in ANOVA otherwise. Figure 3. Mean happiness by happiness groups and education groups (Note) Total N = 2,746. JHS means junior high school and HS means high school. Numbers in parentheses are frequencies of unhappy, average, and happy groups in this order. p<.10, *.05, **.01, ***.001 in ANOVA. who rated their happiness to be 7 as our baseline category, which we call the average group. Correspondingly, if respondents rated their happiness as points 0-6, they were less happy than the average and grouped unhappy. 3) Respondents who chose points 8-10 evaluated themselves more favorable than the average group and are, thus, grouped as happy. Comparing the descriptive statistics of the three subgroups, we find significant differences in their composition (Table 2) except employment status. Differences occur in the expected directions, with a lower share of men and higher share of married respondents in the happy group. Respondents in the happy group tend to have more friends, feel healthier and more free, and have higher income. As already indicated by the overall happiness levels (Table 1), they tend to have lived in their current neighborhood for fewer years than respondents in the unhappy and average group a result that is against our initial expectation. The happy group is slightly younger than the other two groups. With regard to education, we find a somewhat more complex pattern: Differences between the groups are small, albeit significant, with the average group having the longest years of education (13.7 years), followed by the happy group (13.5 years) and the unhappy group (13.2 years). This indicates that the relationship between happiness and education is not straightforward, and that higher educational levels do not necessarily mean a higher chance of happiness. Comparing mean happiness by the educational level (grouped as junior high school, high school, and college) within the three subgroups of the happy, the average, and the unhappy, we find differing trends (Figure 3): In the happy group, happiness levels are slightly lower for the 56

9 Are Happiness and Unhappiness Two Sides of the Same Coin? Table 3. Result of regression analysis on happiness Control variables Male dummy *** Age * Married dummy *** Number of friends *** Years of residence Health (1-5) *** Freedom (1-5) *** Employed dummy Equivalent income Independent variable Years of education * R (Note) Total N = 2,746. Values are standardized coefficients. p<.10, *.5, **.01, ***.001. two higher educational groups, albeit this difference not being significant. Within the unhappy group, however, we see much more variance in happiness levels depending on the educational level, with the differences being significant. Here, lower educational levels are accompanied by lower happiness levels than for higher educational levels. 4) This could imply that there is a strong enhancing effect of education on happiness only for unhappy people, while for happy people more education hardly affects happiness or, if it has an effect, it actually reduces happiness a result that would go against the assumption of Hypothesis Regression analysis of whole sample Before comparing our three subgroups in a multinomial logistic regression, we conduct an OLSregression to disentangle their separate effects for happiness in the overall sample. Results are presented in Table 3. With the exception of years of residence and employment status, most controls have significant effects. The respondents are more likely to feel happy if they are female, younger, married, have more friends, feel healthier, have a higher sense of freedom, or have more income. These effects are consistent with the literature. For our independent variable, we also find a significant effect: Happiness increases with years of education (the coefficient is 0.045) for the whole sample. Next, we test whether this effect is the same for the happy and unhappy respondents. 4.4 Multinomial logistic regression analysis Using the grouped happy, average, and unhappy respondents, we conduct a multinomial logistic regression analysis. We use the average group as our baseline category, against which we compare the other two groups. The results are displayed in Table 4. Each coefficient expresses an effect of the variable on how likely respondents are in the unhappy (or happy) group compared to the average group. We interpret this to mean that the variable increases or decreases happiness levels of unhappy (or happy) people. Note that for the unhappy group, positive coefficients express chances of reducing happiness 57

10 Table 4. Result of multinomial logistic regression analysis on happiness Unhappy group (points 0-6, N = 1,104) Happy group (points 8-10, N = 1,136) Coefficient Odds ratio Coefficient Odds ratio Control variables Male dummy ** Age * Married dummy *** ** 1.58 Number of friends * *** Years of residence * 0.99 Health (1-5) *** *** Freedom (1-5) * *** Employed dummy Equivalent income Independent variable Years of education ** * Log likelihood Cox and Snell R (Note) Total N = 2,746. Baseline category is the average group (point 7, N = 506). p<.10, *.5, **.01, ***.001. (chances of being unhappy instead of average ), while negative coefficients stand for the opposite: chances of increasing happiness (in this case, chances of achieving at least average happiness). For the happy group, positive coefficients indicate the chances of remaining happy (chances of being happy instead of average ), while negative coefficients specify chances of declining happiness (chances of being average instead of happy ). Thus, if coefficients of a variable have different significant (positive or negative) signs in the two groups, the variable has a consistent effect on happiness among happy and unhappy respondents. However, if coefficients have the same significant (positive or negative) signs in the two groups, the variable has contradicting effects on happiness. Our results indicate that both happy and unhappy respondents become happier if they are married (the coefficients are , for the unhappy and happy groups respectively), have more friends (-0.039, 0.050), are healthier (-0.406, 0.281), or have a stronger sense of freedom (-0.148, 0.198). These determinants function in the same ways for the subjective wellbeing of happy and unhappy people. With regard to the other determinants, we found differences: Only unhappy respondents are likely to become unhappier if they are older (0.012), while only happy respondents have significantly higher chances of becoming happier if they are female or live in their current community for a shorter period of time (-0.354, ). We find no significant effects for employment and income a surprising result given the robustness of the effect of income and employment for happiness in previous research. The only variable that showed opposing effects for the happiness of unhappy and happy respondents was education. Unhappy people have significant chances of increasing their happiness by increasing their educational levels (the coefficient is ), whereas happy people risk a reduction in happiness should they increase their educational levels (-0.053). 58

11 Are Happiness and Unhappiness Two Sides of the Same Coin? Figure 4. Test result for Hypothesis 2 (Note) Arrows stand for causal relationships based on significant effects in Table 3 (a dotted arrow means a negative significant relationship). We found that the effect of some determinants on happiness diverge between happy and unhappy respondents. This clearly supports Hypothesis 1: Determinants of happiness differ depending on whether a person evaluates him- or herself as happy or unhappy. Hypothesis 2, however, is supported only in case of the unhappy respondents, for whom we find the expected positive effect of education on happiness. For the happy group, however, results are contrary to our expectations. While we hypothesized a positive (though smaller) impact of education on happiness than for the unhappy group, our results indicate that an increase in education would in fact lower the subjective well-being of happy respondents. By grouping the respondents into happy, average, and unhappy people, our analysis is the first to reveal the contradicting role of education on individual happiness. 4.5 Robustness check The results of our analysis remain robust also when years of education are replaced with a dummy variable of college education. The results also did not change if household income was substituted with individual income. When the analysis is conducted for men and women separately, the general trends remain the same, but effects are more distinct for men possibly an indication of the still stronger expectation towards men to utilize education to feed a family as a main breadwinner. Multinomial logistic regression analyses depend on what subsample is chosen to be the average group. If we use point 6 because of the bottom of two peaks in Figure 2, we find even more significant conflicting effects of education. Similarly, if we use points 6 and 7 to equalize the size of the three groups, the effects become even stronger. Still, if we use point 5 because of the middle point of the alternatives from 0 to 10, the effect of education disappears in the happy group. 5 Concluding discussion 5.1 Summary of analyses In this paper, we tried to answer the question whether determinants of happiness are the same for happy and unhappy people, with a special focus on the roles of education on happiness. Using data of the SSP2015 survey, we classified our respondents into people with above average happiness 59

12 levels (the happy group), people with average happiness levels (the average group), and people with happiness levels below the average (the unhappy group). A descriptive comparison showed that in the unhappy group, respondents with higher education had significantly higher happiness levels, implying an enhancing effect of education on the self-evaluation of unhappy people. In the happy group, on the other hand, we found a small (not significant) reducing effect of education on happiness. A multinomial logistic regression analysis revealed that these opposite effects of education on subjective well-being held true even when controlling for demographics, socio-economic status, and other variables commonly identified as related to happiness in the literature. Education promoted chances of moving from the unhappy to the average group, while reducing the chances of being happy rather than average. This result indicates that happiness is determined in a more complex way than suggested by research so far. Rather than being the same for everybody, determinants of happiness can have different effects depending on an individuals level of happiness, as visualized for education based on our results in Figure 4. Here, we can give an answer to our research question as follows. Answer to research question. Happiness has different determinants and mechanisms for happy and unhappy people. Especially, education plays contradicting roles. 5.2 Implications The overall implications of our study are two-fold: (1) In terms of theory building, the results of our analysis show that happiness is not the same for everyone. Instead, we need to think about it as a multifaceted concept, the determinants of which change depending on how individuals evaluate their life. While happiness has been mostly thought to be a one-dimensional construct, this finding clearly implies that we need to rethink the way subjective well-being is measured, analyzed, and discussed. (2) Within the context of Japanese society, our results point to the specific symbolic value of education when people evaluate their life chances. As educational levels rise, expectations toward occupation, income, and wealth also increase. For those who are unhappy with their current life situation, this symbolic value of education can create a positive feeling of hope, as it comes with the promise of change towards the better in the future. For those who are happy with what they have in the present, however, an increase in expectations triggered by higher education can on the other hand destabilize a previously balanced and positive self-evaluation by creating a sudden thirst for more. The implications of our analysis do not remain purely theoretical, but are of practical importance for policy makers who are interested in raising or at least maintaining levels of subjective well-being in a society. They indicate that the same policies do not work for everyone. Rather, it is necessary to differentiate not only between groups of differing socio-economic status, but also of differing happiness levels. While a general increase of educational levels used to be seen as a promising strategy to raise general well-being (i.e., through the indirect effect of education on subjective well-being via occupational status), this does not always seem to hold true. Education, as the promise of social mobility, can promote well-being of the unhappy, but at the same time it may have the opposite effect on the happy. 60

13 Are Happiness and Unhappiness Two Sides of the Same Coin? 5.3 Next step We are aware that our study comes with a number of caveats. (1) Our analysis here focuses on happiness, while excluding other measures of subjective well-being, such as life satisfaction or mental health. It can be imagined that similarly complex mechanisms can be found here as well something we will focus on in future research. (2) The results applied to Japanese society. In order to generalize our findings, a replication of our analysis with international data is necessary. This can help shed further light on possible differences between happiness construals in Asian and Western contexts (see Hommerich and Klien 2012). (3) In this paper, we used a multinomial logistic analysis. To further substantiate our findings, it seems useful to (a) conduct quantile regression analyses to specify thresholds of happiness; and to (b) add qualitative investigations of how individuals evaluate their happiness. Such a mixed methods approach (see Tashakkori and Teddlie 2002 and Creswell and Clark 2007) can help us better understand how individuals make sense of their lives, and would further increase the practical value of this line of research for policy makers. 5.4 Back to Anna Karenina Finally, to return to our initial reference to Anna Karenina, the results of our analysis paint a different picture from the one described by Tolstoy. He intriguingly suggests that causes of unhappiness are diverse, while what makes people happy is rather unique for everyone. Our results, however, indicate, that what increases people s chances of happiness is just as diverse as what could decrease unhappiness and that staying happy is just as complex as escaping from unhappiness. Especially, education reduced happiness as shown in our analysis for the happy respondents. In other words, happiness and unhappiness turned out not to be two sides of the same coin in a unique mechanism. Instead, they seem to be two different currencies with diverse and different mechanisms. Acknowledgments This work was supported by JSPS KAKENHI Grant Numbers JP16H02045 (as part of the SSP Project, osaka.ac.jp/), JP15H01969, and JP16K The authors thank Guillermina Jasso, Kenji Kawabata, Toru Kikkwa, Hiroko Osaki, and two anonymous reviewers for their constructive comments. Especially Toru Kikkawa, who suggested the idea of grouping the respondents to conduct multinomial regression analyses. The authors thank the SSP Project for the permission to use the SSP2015 survey data. Notes 1) Yuan and Golpelwar (2012) take a similar approach with data from Shanghai by testing the effect of four domains of social quality on life satisfaction by varying levels of the same. 2) We do not include a measure for the seventh dimension of "philosophy of life" as this category is too broad. Some of the controls can be supposed to be consequences of educational attainment (such as employment and income). To count in their indirect effects, we control for them in our analysis. We do so because we assume that happy feelings are complex enough to be affected by both direct (i.e., education) and indirect factors. 3) It cannot be assumed that all respondents in this group think of themselves as unhappy. However, in favour of readability, we call this the unhappy group rather than the group that is less happy than average. 4) It needs to be kept in mind, however, that the range of possible values the mean of happiness can take is broader for the unhappy group (point 0-6) than for the happy group (8-10). Therefore, to test whether there really is a stronger/different relationship between education and happiness for unhappy as opposed to happy respondents, 61

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15 Are Happiness and Unhappiness Two Sides of the Same Coin? and Happiness: The Moderating Role of Domain Importance, Personality and Individual Differences 86: Thoits, P. A. and L. N. Hewitt, 2001, Volunteer Work and Well-being, Journal of Health and Social Behavior 42: Tsang, L., C., Harvey, K. Duncan, and R. Sommer, 2003, The Effects of Children, Dual Earner Status, Sex Role Traditionalism, and Marital Structure on Marital Happiness Over Time, Journal of Family and Economic Issues 24(1): Winkelmann, L., and R. Winkelmann, 1998, Why are the Unemployed So Unhappy? Evidence from Panel Data, Economica 65(257): Yuan, H. and M. Golpelwar, 2012, Testing Subjective Well-Being from the Perspective of Social Quality: Quantile Regression Evidence from Shangai, China, Social Indicators Research 113(1): Jun Kobayashi. Professor, Department of Contemporary Societies, Seikei University Kichijoji-Kitamachi, Musasino-shi, Tokyo , Japan. Areas of interest: social inequality, social psychology, well-being. Carola Hommerich. Associate Professor, Department of Sociology, Graduate School of Letters, Hokkaido University. N.10 W.7 Kita-ku, Sapporo , Japan. Areas of interest: risk society, social inequality, subjective well-being. 63