Diener and Oishi reported that across nations, income equality was not associated with higher SWB, but Oishi, Kesebir, and Diener did find such a relationship over time in the US. They found that the association between inequality and SWB was particularly strong for poorer respondents, and it was mediated by feelings of unfairness and distrust.
Burkhauser, De Neve, and Powdthavee found that when the top one percent of individuals in terms of income earns a proportionately higher share, that the population life satisfaction declines and negative affect increases. Income inequality also might have very negative effects on happiness in societies where it is high and persistent Graham, Relatedly, Cheung and Lucas found that social comparison effects of income are stronger when income inequality is high.
It appears that the effects of objective income inequality may depend on factors such as whether people believe in income mobility in their nation, and whether they believe that inequality is unfair. Importantly, Zyphur, Sarafides, Tay, Connor, Diener, and Pierides found that greater income equality due to redistribution from government policies largely dampened the negative effects of market inequality on SWB.
In other words, the assumed causal direction goes from income to SWB. Of course, most of the research reviewed above is correlational in nature, and very little of it has features that allow for a sophisticated analysis of causal direction. Yet some research has been conducted that speaks to causality. In a classic though over-interpreted study, Brickman, Coates, and Janoff-Bulman used the occurrence of a relatively random event—a large lottery win—to examine the effects of income change on happiness.
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They reported that lottery winners were not significantly happier than a comparison group, and concluded that the increase in income did not cause a corresponding change in happiness. Furthermore, in a longitudinal analysis, Gardner and Oswald found that SWB rose among those receiving substantial inheritances. These findings suggest that rising income can in fact raise SWB, even over relatively long periods.
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In studies where people have received supplemental income in controlled experiments, the outcomes have been mixed. Haushofer and Shapiro studied unconditional cash transfers and found that people in a Kenyan village receiving an income supplement had higher SWB, including higher reports of happiness and life satisfaction and lower reports of stress and depression.
These patterns were confirmed by reduced cortisol in the recipients, but only in households where females received the transfer.
A year after the payments ended the effects on SWB were virtually gone. Additional cash transfer studies are being coordinated around the world. However, a note of caution is in order regarding the generalizability of the lottery studies and the cash transfer experiments. For one thing, it might be different in terms of feelings of self-esteem and mastery to passively receive money rather than to earn it.
For another, people may encounter different issues such as envious neighbors and friends than is encountered when people earn income. Thus, although these studies are strong in terms of inferring causality, they raise questions regarding generalizability.
Measurement of Well-Being
A final issue that impacts research and theorizing on the links between income and SWB is that this association varies in different contexts. This suggests that there may be no single explanation for the association between the two variables. The line between the two is sometimes blurred, and in some cases material purchases may produce more frequent happiness over time Nguyen, ; Weidman, Additional research suggests that there are individual differences in the associations between income and SWB.
Soto and Luhmann found that personality moderates the effects of income on SWB.
For example, neurotic individuals react more strongly to their level of incomes and changes in their incomes. Cheung and Lucas found that age moderates the association, with middle aged participants showing stronger associations than older or younger participants possibly because income may not be the best indicator of financial health among the young or the old. Social factors might also alter the influence of money on happiness. For example, Thoits and Hannan found that those whose incomes were increased in a negative income-tax experiment reported greater distress after the intervention, compared to those who did not receive the payments.
Perhaps the money caused stress because neighbors reacted negatively. Kahneman and Deaton uncovered another moderator in that negative life events such as illness and divorce have a much more negative impact on the poor than the rich. Despite the complexities involved in the study of income and its association with SWB, several clear conclusions can be drawn, both in terms of what researchers already know and what debates will require more empirical evidence to resolve.
First, the association between income and SWB is consistently positive in cross-sectional studies, with small to medium effect sizes within nations and large effect sizes when ecological correlations are examined e. Second, it appears that income is more strongly associated with judgment-focused measures like life satisfaction than it is with more affective measures, though differences in the quality of the measures that are available cannot always be ruled out as an explanation of these differences.
Third, although early reviews concluded that people quickly reach a satiation point beyond which increased income is no longer associated with increased SWB, more recent research with large samples and sophisticated designs has challenged that conclusion; thus, more research is needed to determine whether such a satiation point really exists. Similarly, although considerable research has been conducted to determine whether the effects of income are relative or absolute, and although it seems likely that both types of effects play a role, more research is needed on the relative impact of absolute income and comparison standards.
Future research is also needed to clarify whether people adapt to changes in income.
Finally, research clearly shows that various factors, including the way that people use their income, along with various individual difference variables, moderate the association between income and SWB. A greater focus on these moderating factors will help clarify the processes that account for this complex and widely studied association. Another demographic characteristic that is frequently found to be associated with SWB is religiosity. For example, Diener and Clifton found a small but significant association between religiosity and SWB in broad representative samples.
Similarly, Tay, Li, Myers, and Diener examined the association in multiple large samples from the USA and other nations, and they found that religiosity was consistently associated with higher SWB. Like the association with income, research on religion and SWB has turned to understanding why the association exists and whether there are contextual factors that influence its strength or even direction. In addition, because the construct of religiosity itself is not as concrete or clear as income, some research focuses on whether distinct types of religiosity e.
In terms of process models, Pargament noted that there are many potential mediators of religiosity and SWB, including the social support that comes from belonging to a religious community, the sense of meaning that may come with some religious beliefs, and potential benefits from specific behaviors such as prayer or attendance at religious services. Ellison found that strong religious belief systems appeared to be directly associated with SWB, whereas religious attendance and private devotion appeared to be only indirectly related to SWB through strengthened religious beliefs.
However, recent work also raises the possibility that religion helps people in different circumstances for different reasons, and that the effects of religiosity vary by populations and conditions. Recent findings suggest that religion may only benefit some individuals, and only individuals living in certain circumstances. For example, Diener, Tay, and Myers found that religion was related to higher SWB across four major religions, but this effect was strong in nations with difficult circumstances such as widespread hunger and low life expectancy.
They found that in wealthy nations, religious people did not have higher SWB than less religious people. Hoverd and Sibley replicated this finding in New Zealand, where they found that people living in deprived neighborhoods had higher SWB if they were religious, but in wealthier neighborhoods both groups were comparable and relatively high in SWB.
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Along the same lines, Zuckerman, Li, and Diener suggested that effective government might decrease the popularity of religion when it takes over certain functions such as security that religions have traditionally helped fulfill. Diener et al. A path model with these three mediators explained the association between religiosity and SWB for all three major types of SWB—life satisfaction, positive feelings, and low negative feelings.
The direct paths from religiosity to SWB were small in this model, suggesting that the mediating variables of social support, respect, and meaning together explained most of the effects. In terms of the influence of being respected, Diener et al. Religious people in religious nations are respected and fit in, whereas nonreligious people may not be so readily accepted. Consistent with these ideas, Graham and Crown found that religion appears to serve different functions for different socioeconomic groups.
Chang found in Taiwan that although religious attendance was associated with higher SWB, the pattern of associations was moderated by Christian versus Eastern religious traditions. Thus, it appears that religiosity is a frequent but not a universal predictor of higher SWB, and its effects and mediators depend to some degree on culture and the life circumstances of respondents. Although considerable research has focused on social relationships, social scientists have also amassed a large amount of evidence about the extent to which other characteristics are linked with self-reported subjective well-being.
For instance, much research has focused on trends in SWB over the lifespan. Although early reviews emphasized that there was not a strong association between the two e. However, even the basic finding that life satisfaction shows a U-shaped trajectory over the lifespan is not without controversy. Although this pattern is often found when large-sample studies are used, this is not always the case. For instance, Steptoe, Deaton, and Stone found that in many countries around the world SWB declines linearly with age, and it is only in wealthy Western nations that the U-shaped curve is found.
Furthermore, even in these countries, some studies fail to find the U-shaped curve e. Thus, future research can clarify when and why this age-related pattern occurs. Other demographic characteristics also show relatively small associations with well-being. It is important to note, however, that most investigations into demographic predictors of subjective well-being focus exclusively on the individual-level associations i.
It is also possible to examine the links between population-level characteristics and well-being outcomes. Often, these associations differ depending on the level of analysis.
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For instance, Lawless and Lucas examined the predictors of county-level well-being in the United States. In contrast to results at the individual level, county-levels of educational attainment were one of the strongest predictors of aggregate life satisfaction across regions with aggregate correlations in the range of. More and more studies are examining regional differences, and it is likely that new insights about the characteristics of happy communities will emerge from this work. The biggest sex differences tend to be found for affective measures rather than cognitive judgments of life satisfaction.
As is true of many demographic predictors especially those with small overall effects , the direction of this association may vary across contexts. For instance, Stevenson and Wolfers presented evidence that once small sex differences in life satisfaction in the United States have grown in recent years. Tesch-Romer, Motel-Klingebiel, and Tomasik found that the direction of sex differences depended on the nation.
In countries with high support for gender equality, greater gender equality in the labor market was associated with smaller gender differences in SWB. In contrast, in nations where citizens did not support gender equality, greater labor market equality was associated with larger differences in SWB between men and women. Similarly, Zuckerman, Li, and Diener found that for life satisfaction and positive affect, sex differences were largest in nations where conditions had become moderately favorable for women. Sex differences were smaller in traditional societies and in nations where women have made the most progress.
However, for negative affect, sex differences were largest in nations that had conditions that were most favorable to women. Thus, it appears that sex differences in SWB are not universal, are often small, and depend on the cultural values and conditions in societies. Various health conditions—both positive conditions such as physical strength and mobility and negative conditions such as physical disease or injury—have the potential to significantly impact the day-to-day lives of people who experience them.
Initial research on the topic of health conditions often concluded that health played only a minor role in well-being judgments Diener et al. According to this early research, only self-reports of health were consistently associated with SWB, but this association diminished in size when more objective reports of health were used; suspicion about the value of self-reports often led to the conclusion that the moderate association when self-reports of both constructs were used might be artifactual.
In addition, it has become clear that the link between health conditions and well-being outcomes is stronger than once thought. For instance, literature reviews often mention the Brickman, Coates, and Janoff-Bulman study in which patients with spinal cord injuries were compared to lottery winners and a group of controls. The surprising finding from this study—one that seemed to suggest a limited impact of health conditions—was that, according to the authors, the three groups barely differed in their levels of SWB. However, this conclusion was somewhat impressionistic, and by actually calculating effect sizes, Lucas showed that that the differences between the spinal-cord-injured group and the other two were substantial with standardized mean differences around.
Furthermore, other studies have shown that disabling conditions—especially those that are more severe or those that have a strong effect on mobility—are associated with relatively large decrements in SWB again, see Lucas, , for a discussion and evidence from longitudinal studies. Thus, although early reviews often downplayed the role of health in SWB judgments, it has become clear that health conditions can have a major impact on the well-being that people report.
The overview presented above shows that considerable research has been conducted that examines the correlation between various components of SWB and a wide range of demographic factors and life circumstances. This research benefits from the fact that many large-scale and wide-ranging surveys include measures of SWB, which means that high quality data are available, and these data can provide an initial picture of who does and does not report high levels of SWB.
The evidence reviewed above suggests that high SWB is consistently associated with high levels of income, strong social relationships, and—at least in some contexts—with high levels of religiosity.