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.
Previous studies have estimated the relationship between social network site (SNS) usage and social capital using Williams’s Internet Social Capital Scales (ISCS). These studies have found that SNS usage is positively associated with social capital. However, ISCS mainly focuses on emotional support, such as helping solve personal problems and making users try new things. Because social capital can also promote socioeconomic support, which can be obtained through SNS usage, the relationship between SNS usage and socioeconomic social capital should be elucidated. In this study, Lin’s definition was utilized to measure the socioeconomic social capital via a position generator. Additionally, previous studies have only used general regression to estimate the association between SNS usage and social capital. However, this could involve crucial problems in estimating. To avoid these problems, propensity score matching was used to estimate the “net effects” of SNS usage on socioeconomic social capital. A dataset (N = 2,255) collected nationwide in the United States was utilized in the analyses. The results suggest that SNSs negatively affect users’ socioeconomic social capital, which means that SNS usage may decrease users’ socioeconomic support and lead to disadvantageous socioeconomic positions. In other words, we may not benefit from SNS usage.