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Abstract

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

Subjective well-being is a broad construct that reflects an individual's subjective evaluation of the quality of his or her life. Psychologists know a great deal about the causes and correlates of well-being, but some important misconceptions have developed and are often repeated. The purpose of this article is to address the evidence for four such misconceptions that we believe reflect ‘myths’ about subjective well-being. These myths include the idea that well-being measures are strongly influenced by irrelevant contextual factors, the idea that money is not an important correlate of well-being, the idea that social relationship variables are a particularly strong correlate of well-being, and the idea that well-being cannot change.

An important goal of most scientific endeavours is to improve people's lives. Medical doctors seek treatments that will not only lengthen life, but also those that will promote patient comfort, energy, and a sense of well-being. Political scientists hope to understand the processes that promote efficient and effective government because these types of governments are thought to create satisfying conditions for their citizens. Economists attempt to discover the laws that govern micro- and macro-economic processes; but again, this work is done with the belief that economic conditions can serve a greater good: thriving economies should lead to flourishing individuals. Thus, quality of life – the psychological construct that reflects the global evaluation of a person's life as a whole – is a common currency that can link all sciences.

One important way that quality of life can be evaluated is through subjective evaluations of a person's life as a whole. The field of subjective well-being (SWB) focuses on such evaluations. Although the field is relatively new, research findings have built up rapidly. There are now numerous reviews of the field where catalogues of correlates can be found (e.g., Argyle, 1999; Diener, 1984; Diener, Suh, Lucas, & Smith, 1999; Eid & Larsen, 2008; Kahneman, Diener, & Schwarz, 1999). Occasionally, however, certain ideas about SWB propagate – either among lay people or among scientists – even when very little empirical support for these ideas exist. These ideas often have a kernel of empirical truth, but the strength of the findings have been exaggerated, or the implications have been overstated. The purpose of the current paper is to discuss some of the more frequently cited findings or intuitively appealing ideas that do not hold up to empirical scrutiny. Thus, we will not provide a general overview of the field, as this information is available elsewhere. Instead, we focus on four ideas that we believe reflect myths about SWB. For the purposes of this paper, we define myths as often-repeated claims that are not supported by empirical evidence. Because each of the myths we address does have some kernel of truth, we will attempt to be explicit about what part of the idea is supported by empirical evidence, and what part is a myth.

Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

If well-being measures are to be used to evaluate the quality of an individual's life, then these measures must be reliable and valid. However, well-being, like most other psychological constructs, cannot be seen. Thus, there is no gold standard measure against which to compare our measures. This fact has led to some suspicion about whether well-being can in fact be measured. One common criticism is that responses to these measures can be influenced by irrelevant contextual stimuli. If so, responses will vary across occasions, and no stable information about a person's long-term levels of well-being could be obtained. For instance, Schwarz and Strack (1999) suggested that short-term stabilities were extremely low and that context effects were strong. They concluded that ‘there is little to be learned from global self-reports of well-being ... [W]hat is being assessed, and how, seems too context dependent to provide reliable information about a population's well-being’ (p. 80).

In support of this argument, Schwarz and Strack (1999) cited a number of studies that show context effects. For instance, in one of the most famous studies, Schwarz and Clore (1983) found that life satisfaction reports varied depending on whether people were asked to report their satisfaction on a warm and sunny day or a cold and rainy day. A second study that is often cited in support of context effects is a study by Strack, Martin, and Schwarz (1988). In the Strack et al. study, participants who were asked about their satisfaction with dating before being asked about their satisfaction with life showed stronger correlations between the two questions than did participants who received the questions in the reverse order. The explanation for this phenomenon is that dating satisfaction was made salient when the question was presented, and this led participants to incorporate dating satisfaction into their global life satisfaction ratings.

Although these studies provide important insight into the processes that underlie well-being judgment, it is not clear from these individual studies how strong these context effects are. For instance, although the Schwarz and Clore (1983) weather study has been cited 659 times as of this writing, as far as we can tell, not one of these studies has replicated the weather effect.1 This is not to say that the original study or its conclusions were flawed, just that the lack of replications makes it very difficult to evaluate the size or robustness of the effect. Similarly, in regards to the question-order effects and the more general issue of short-term stability, it appears as though the effects of such manipulations tend to be quite weak. Schimmack and Oishi (2005) conducted a meta-analysis of studies that used the Strack et al. (1988) question-order manipulation, along with five new replication studies. They found that these item-order effects tend to be quite small. In addition, Schimmack and Oishi showed that short-term stabilities tend to be moderate to strong (ranging from 0.55 to 0.65 for periods as long as a year). This suggests that both in typical testing situations and in more controlled settings that are designed explicitly to pull for context effects, such effects tend to be quite small.

This evidence corresponds well with additional studies designed to assess the relative impact of transient mood and stable traits on well-being judgments. For instance, Eid and Diener (2004), administered well-being measures (along with current mood measures) three times over the course of a 12-week period. This enabled them to separate trait well-being variance (which is stable across all occasions) from state well-being variance (which changes from occasion to occasion). Furthermore, their design allowed them to determine how much of an impact current mood has on the various components of well-being judgments. Eid and Diener showed that when a well-being measure is administered, most of the variance (between 74% and 84%) is stable trait variance that is consistent over relatively long periods of time. Furthermore, only a very small percentage is reliable state variance that is unique to the specific occasion, and even this transient influence is only weakly related to current mood.

It is likely that the context effects that Schwarz and Strack (1999) discuss do exist and do contribute to well-being judgments. Therefore, studies that examine these processes make a strong contribution by identifying some of the processes that go into a well-being judgment. Thus, there is a kernel of truth to this myth. However, the question of whether these processes exist is distinct from question about the strength of the impact that these processes have on the validity of well-being judgments. If context effects are small, we should expect moderate to strong stability in well-being measures (particularly over relatively short periods of time), combined with sensitivity to differences in life circumstances and responsivity to changing life circumstances; and this is what we typically find (see Lucas, 2008, for a review). Thus, the myth is that such context effects have been shown to be strong enough to affect the validity of SWB measures. To be sure, researchers must not blindly accept self-report measures. Especially when additional predictors and outcomes are measured using the same technique, shared method variance may inflate correlations. Thus, multi-method studies are always desirable (see Eid & Diener, 2005). However, self-report methods, particularly when used in combination with other techniques, provide useful information about a person's subjective quality of life.

Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

A continuing debate in the literature on SWB is whether income and happiness are related in any important way. Intuition suggests that money and the resources it provides should play an important role in happiness. Money can open doors to many of the things that people want in life. Access to wealth can bring with it access to material goods, pleasurable experiences, better healthcare, and increased security. However, psychologists often cite evidence that the correlation between income and well-being is not large and conclude that money is not important for well being. To reconcile these discrepancies between intuition and the empirical evidence, we believe it is important give special attention to how we interpret the available data.

First, it is important note that the size of the income/happiness correlation varies depending on the unit of analysis that is examined. For instance, when entire nations are compared, average income tends to correlate very strongly with happiness (Diener et al., 2008). In contrast, when the same nation is examined over time, the average income in each year tends not to correlate at all with the average happiness in that year (Easterlin, 1995). But in general, when people wonder about the extent to which money and happiness are related, they are referring to within-nation correlations between individual levels of income and individual levels of happiness. In other words, do wealthy people tend to be happier than poorer people?

To get a sense of the absolute size of the association between income and happiness, Lucas and Dyrenforth (2006) turned to data from the General Social Survey (GSS; Davis, Smith, & Mardsen, 1999) and several reviews in the literature. The GSS includes responses from over 30,000 Americans between 1972 and 1998. In this large sample, real income was correlated 0.18 with well-being. Similar results were obtained in a review of 11 studies conducted by Diener and Biswas-Diener (2002). They found an unweighted average correlation of 0.20. These estimates are also consistent with two existing meta-analysis. The first examined the association between socioeconomic status and well-being among the elderly (Pinquart & Sörensen, 2000). The average correlation between income and happiness was found to be 0.21, and the average correlation between income and life satisfaction was 0.18. A second meta-analysis estimated the average correlation between SWB and income to be 0.17 (Haring, Stock, & Okun, 1984). Thus, across various sources, there is consistent evidence that the correlation between happiness and income is somewhere between 0.17 and 0.21.

To interpret these effects, researchers have traditionally compared these estimates to the rules of thumb proposed by Cohen (1988) and concluded that the association between income and well-being is small. This would suggest that money is unimportant for happiness and that peoples’ intuitive beliefs regarding this association are incorrect. However, correlations are notoriously difficult to interpret (Rosenthal & Rubin, 1982). In fact, small correlations can hide associations that would otherwise be considered large and practically important.

For instance, if we want to know whether rich people are considerably happier than poor people (or even middle-income people), it is difficult to use the size of a correlation to answer this question. In the case of income and happiness, small correlations can translate into large mean differences in happiness between the rich and the poor. Lucas and Schimmack (forthcoming) demonstrated this using data from a large German panel study. Like previous studies, Lucas and Schimmack estimated the bivariate correlation between income and happiness. In addition, they estimated the average standardized life satisfaction score for distinct groups of individuals with varying levels of income. Despite the fact that the correlation between income and life satisfaction was just 0.18 (generally interpreted as a small effect), there were large mean differences in happiness across the income groups. Participants in the richest group (those who made the equivalent of over $200,000 a year) reported happiness scores that were over three quarters of a standard deviation higher than those in the poorest group (those who made less than $10,000 a year). In addition, the richest group was over one half of a standard deviation higher than those who make an average amount of money. This pattern was also replicated in the World Values Survey, a multi-national cross-sectional study.

We want to emphasize that there are no statistical tricks being played here. Because correlations are difficult to interpret, we are simply presenting the data in a different way, namely by reporting standardized life satisfaction scores for people at different income levels. In the social sciences, we are often encouraged to consider values of a predictor that are just one standard deviation above or below the mean, as these reflect realistic estimates of the amount of variance that typically exists in a measure. However, it can often be useful to consider values that are farther from the mean, if these reflect meaningful points of comparison. In the current example, people who make $200,000 a year have incomes that are four or five standard deviations above the income of the very poor. The fact that that these people report happiness scores that are eight tenths of a standard deviation higher than those reported by the poorer participants in the sample is exactly what we would expect based on a correlation of 0.18 (0.18 × 4 standard deviations = 0.76 standard deviation difference). Although one might interpret a correlation of 0.18 to be small, the large mean differences across people in distinct income groups leads to a very different interpretation of this effect. In fact, these large mean differences support the intuitive belief that wealthy people are quite a bit happier than poor people or even those with average incomes.

Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

If you were to ask someone what factors predict how happy a person is, it is likely that many of the answers would relate to some form of social relationship. Intuition and societal beliefs suggest that friendships, romantic partnerships, and strong family relationships are of primary importance for leading a happy and satisfied life. Psychologists seem to agree, as social relationships are continually cited as central to SWB. For example, according to one review of the literature on well-being, ‘social relationships have a powerful effect on happiness and other aspects of well-being, and are perhaps its greatest single cause’ (Argyle, 2001, p. 71). While other demographic factors such as income, level of education, and health status are dismissed as having only weak associations with SWB, social relationships are repeatedly held up as a strong and primary cause of happiness.

We do not doubt that social relationships influence well-being. A great deal of correlational and experimental evidence supports the contention that happiness is influenced by social factors. Measures of sociability and extraversion, the amount of time spent in social interactions, social network size, and even marital status are all reliably correlated with ratings of happiness and well-being. However, we do question the claims regarding the size of these associations, particularly in comparison to alternative predictors such as income or health. For instance, most reviews conclude that social relationships have a much stronger association with well-being than income. However, as we review below, the data do not support this conclusion (see Lucas & Dyrenforth, 2006, for a more thorough review).

Because this is likely to be the most controversial of the points we wish to make, we want to be very explicit about the precise features of this belief that we think reflect a myth. Ultimately, our concerns boil down to two issues. First, many reviews that focus on the associations between SWB and social relationships fail to mention effect sizes. But when these effect sizes are calculated – at least for objective relationship variables – they tend to be quite small and similar to effect sizes for other objective predictors including income and health. Second, the documented associations that do have large effect sizes tend to involve self-reports of variables like relationship satisfaction. And as we discuss, there are some compelling alternative explanations for why these variables might be linked with happiness. We are certainly open to the possibility that social relationships will eventually be shown to be the strongest predictors of well-being. Our point is that much of the existing evidence does not show what it has been claimed to show.

Lucas and Dyrenforth (2006) used data from two meta-analyses and the GSS to estimate the size of many different relationship effects. First, they examined whether the number of friends an individual has is related to happiness. According to one meta-analysis of research on social activity and well-being, the scope of an individual's social contact (including the size of his or her social network) correlated 0.16 with happiness and life satisfaction (Okun, Stock, Haring, & Witter, 1984). Another meta-analysis of older adults found that the quantity of social activity correlated 0.12 with life satisfaction and 0.17 with happiness (Pinquart & Sörensen, 2000). Within the GSS, the number of friends reported was correlated with general happiness (r = 0.13; Lucas & Dyrenforth, 2006). Although this estimate is consistent with those reported in the literature, it is important to note that the effect is actually smaller than the effect of income in the same sample (r = 0.18).

Analyses using additional variables assessing the frequency of contact do not do any better when predicting SWB. For example, participants in the GSS reported the frequency with which they spend a social evening with relatives, neighbors, friends, parents, and siblings. They also reported how often they visited their closest friend and how often they spoke with that friend on the phone. All of the correlations between these variables and happiness were very small, with none larger than 0.06 (see Lucas & Dyrenforth, 2006).

Both intuition and theory suggest that in addition to the sheer amount of time spent with other people, the type and closeness of social partners should moderate the impact that social engagement has on well-being. For example, spending time with a good friend with whom you can confide may be more beneficial than being surrounded by several people who are mere acquaintances. However, data from the GSS do not support this idea. Respondents were asked to indicate the number of people on whom they could call if they had a problem. The correlation with well-being was again very low, only 0.05. Overall, this is consistent with other GSS and meta-analytic results regarding social activity that show small associations (notably, even smaller than those for income) with well-being (see Lucas & Dyrenforth, 2006).

A final type of relationship that speaks to the association between social relationships and well-being is that of marital status. If marital status can serve as a proxy measure of a strong social relationship then differences in well-being across marital status might confirm the importance of relationships for well-being. In fact, most studies show a consistent association between marital history and well-being. For example, Mastekaasa (1994) used data from 19 countries to compare well-being for married, divorced, widowed, and never married individuals. The married group was consistently highest in SWB, while the divorced and widowed groups were consistently the lowest. Two different meta-analyses have confirmed that this association is reliable (Haring-Hidore, Stock, Okun, & Witter, 1985; Wood, Rhodes, & Whelan, 1989). These differences support the idea that marital status predicts increased well-being.

However, the size of the marital status effect is not large. In the GSS, the correlation between happiness and marital status is 0.23, only slightly higher than that for income. A meta-analytic estimate was even smaller, with a correlation of just 0.14 between happiness and marital status (Haring-Hidore et al., 1985). Using only the married and never-married participants reduced the effect size even more (r = 0.09). Furthermore, there is some evidence that the causal direction is reversed – happier people may be more likely to become married than are unhappy people (Lucas, Clark, Georgellis, & Diener, 2003; Stutzer & Frey, 2003).

The evidence we have reviewed suggests that social relationships do impact SWB. Highly sociable and extraverted people experience more positive affect than less sociable individuals. People who spend more time with others, or have more friends are happier than those who spend more time alone or have few friends. And married people report higher life satisfaction than people that have experienced divorce or widowhood. However, the size of these effects is not commensurate with claims that social relationships are a particularly strong predictor of well-being. Correlations between the number of friends, frequency of contact, marital status, and actual social activity are generally small, between 0.10 and 0.20. In fact, many of these effect sizes are smaller than those for other variables often interpreted as unimportant (e.g., income).

In light of the empirical evidence that the effects are not large, it is important to consider why social relationships have held such an esteemed reputation as a cause of SWB (e.g., Argyle, 2001). We have argued that both methodological and interpretational issues might be at play (Lucas & Dyrenforth, 2006). First, the beneficial effects of social relationships on SWB are often discussed along with outcomes from other domains such as health and even longevity (e.g., House, Landis, & Umberson, 1988; House, Robbins, & Metzner, 1982). In this context, the robustness of these effects across a variety of domains seems impressive, even if the actual effect sizes for well-being are small.

It is also possible that perceptions regarding the power of social relationships may be due more to the effects of relationship quality rather than the simple existence of social relationships. Our earlier review (Lucas & Dyrenforth, 2006) focused on objective measures including the existence of social relationships and the amount of time people spend with social relationship partners. We did not rule out the impact that relationship quality may have on estimates of effect size. However, it is important to note that quality of relationships is often assessed using self-report measures of relationship satisfaction or related variables. These measures share considerable method variance with self-report well-being variables, which likely inflates the size of this effect. Satisfaction with one's income also correlates quite strongly with satisfaction with life, but few confuse satisfaction with income for a ‘quality-of-income’ measure. Instead, these associations would generally be interpreted as evidence for top-down effects, whereby overall happiness makes one satisfied with a wide range of domains within a person's life. We believe that similar caution is warranted when interpreting self-report measures of satisfaction with relationships as predictors of SWB. To demonstrate that current views about the importance of social relationships do not reflect a myth, it will be necessary to show robust associations between non-self-report relationship quality measures and SWB with effect sizes that are larger than are typically found for variables like income and health.

In our review, we have described evidence that the effect for social relationships on well-being is not as large as might be expected based on claims in the literature. It is worth repeating, however, that we are not arguing that social relationships are unimportant for well-being. Although the effect sizes are small by traditional standards, we recognize that small effects can be very important (Meyer et al., 2001). However, we believe it is important to view this evidence objectively and that a clear understanding of size of this effect (as well as those for other variables) is important to making progress toward understanding the factors that influence well-being.

Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

The final myth we address concerns the idea that people adapt to all life circumstances and that happiness cannot change. This may be the most important concern regarding the practical utility of well-being measures. For if people inevitably adapt, then few life circumstances will show strong correlations with well-being. Therefore, it will be impossible to use these correlations to inform theories of basic human needs, to provide practical advice to people who want to improve their levels of well-being, or to guide policy decisions. Fortunately, recent evidence suggests that happiness can, in fact, change.

The myth regarding adaptation has arisen from at least four distinct lines of research. First, decades’ worth of research shows that objective measures of life circumstances correlate only weakly with measures of SWB (Diener et al., 1999). It is thought that if circumstances such as income, health, and marital status had a strong impact on happiness, the correlations between SWB and these variables should be strong (though see our above discussion for a reinterpretation of some of these effects). Second, research on personality predictors of SWB shows that all well-being components are moderately to strongly correlated with personality variables such as extraversion, neuroticism, self-esteem, and optimism (Lucas, 2008). Because personality traits are reasonably stable over time, and because they have some genetic basis, these moderate correlations suggest that much of the influence on happiness is inborn and stable over time.

A third line of research that suggests that happiness cannot change comes from studies of the stability of happiness. Most existing studies show that even over long periods of time, and even in the face of changing life circumstances, happiness is reasonably stable (e.g., Costa, McCrae, & Zonderman, 1987). For instance, research using long-running panel studies shows that even over periods as long as fifteen or twenty years, life satisfaction ratings are moderately stable (Fujita & Diener, 2005; Lucas & Donnellan, 2007). Given that some changes in circumstances are likely to occur to most people over such long periods of time, the remarkable stability has led some to conclude that well-being variables are more like personality traits than reactive constructs that can be influenced by life circumstances.

To some, the most convincing evidence regarding the inability to change happiness comes from behavioral genetic studies. Twin and adoption studies consistently show that happiness is moderately heritable with estimates typically ranging between 0.40 and 0.50 (see Lucas, 2008, for a review). More importantly, Lykken and Tellegen (1996) suggested that events may temporarily affect reports of happiness but that people will inevitably return to their genetically determined baseline. In support of this idea, they showed that the heritability of the stable component of happiness was quite high – about 0.80. They concluded that although short-term levels of happiness fluctuate, long-term levels of happiness should be difficult to change.

These four lines of research are important because the help show the wide variety of factors (some of which are in-born) that affect levels of happiness and well-being. But they are often interpreted to mean that life circumstances do not matter, and this conclusion is not appropriate. Take for instance, the findings on heritability. There are a number of reasons why we should not interpret a heritability coefficient of 0.50 to mean that half of our happiness is stable and cannot be changed (Diener, 2008). For one thing, heritability coefficients describe the extent to which variance in a specific population (with a specific range of environments) can be attributed to genes. This tells us little about the potential effect of environmental factors on specific individuals. Furthermore, heritability coefficients tell us little about the processes by which genes affect well-being. It is entirely possible that the causal path flows from genes to life decisions to happiness. If so, then these decisions could be changed with intervention, and increases in happiness could result. Thus, heritability studies and other research programs that investigate the inborn factors that influence well-being are interesting and important, but they should not be interpreted to mean that happiness cannot change.

Furthermore, each of the lines of research described above provides suggestive, but only indirect support for the idea that life events do not matter. A more direct way to address this question comes from studies that explicitly examine the impact of life events on happiness. The ideal technique for doing so is a prospective design where pre- and post-event levels could be tracked. Psychologists and economists have begun to use long-running panel studies to address these questions using nationally representative samples. Our studies show that happiness does change following life events, though the pattern of these changes varies depending on the event in question. For instance, Lucas et al. (2003) assessed life satisfaction before and after marriage. They showed that happiness increased around the time of the marriage, but that happiness levels returned to baseline after just two years. However, other events lead to a different picture. For instance, they showed that widows and widowers experienced large drops in happiness following the loss of their spouse. Although they did eventually return quite close to their initial baseline, this process of adaptation took about seven years to occur. Similarly, the onset of divorce was associated with a moderate drop in happiness, followed by some adaptation. However, the divorced individuals were significantly lower than baseline, even at the peak of their adaptation (Lucas, 2005). Thus, unpleasant marital events can have lasting – and sometimes permanent – effects on SWB.

Even more substantial reactions were observed following non-marital events. For instance, Lucas, Clark, Georgellis, and Diener (2004) examined individuals who experienced a bout of unemployment and then became reemployed. Even though these individuals eventually found another job, the single bout of unemployment was associated with a lasting drop in happiness. More recently, Lucas (2007a) showed that the onset of a disability is associated with moderate to large drops in life satisfaction. For instance, individuals who report being 100% disabled (using a metric designed to assess severity of disability when considering work benefits) showed drops in life satisfaction that were greater than a full standard deviation. In addition, these life satisfaction levels did not show any evidence of adaptation – they remained at the lowered level for the remainder of the study. Thus, life events can have substantial associations with changes in SWB.

An important caveat that must be made concerns the fact that there are large individual differences in the reactions that people have to life events. Even for events like marriage, where the average person adapts within about two years, there are some individuals who experience lasting boosts after the event, whereas other individuals experience lasting drops in happiness. This suggests that a simple investigation of the average effect may be misleading. In addition, it suggests that additional research is needed to determine the moderators of this effect (Lucas, 2007b). In any case, studies that examine life events using prospective data show that life events do matter and that happiness can changes.

Summary

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

Considerable progress has been made in identifying robust correlates of SWB. These research findings help to clarify the processes that underlie well-being judgments, which, in turn, helps researchers and practitioners use SWB measures to inform theories and practical problems in other areas. However, there is not always a direct correspondence between the weight of the empirical evidence and the lay person's (or even the psychologist's) perception of this evidence. The goal of the current chapter was to re-evaluate some findings that we believe have been misstated in the literature or popular press.

Specifically, we addressed four findings that we believe have been overstated in existing reviews of the field. First, although some social psychological research suggests that self-reports of well-being are susceptible to strong context effects, the larger body of evidence examining the reliability and validity of these measures shows that they are quite good. Second, although researchers often conclude that the association between income and happiness is quite small, our re-analysis shows that this small correlation can translate into large differences between distinct income groups. Third, although psychologists often cite social relationships as the single best predictor of happiness, the size of these effects is often similar to other effects including the correlation with income or health. Finally, although the evidence regarding the stability and heritability of happiness measures has led some to conclude that happiness cannot change, more recent studies that use prospective data to examine reaction and adaptation to life events challenge this conclusion. Life events matter and happiness does change.

Short Biography

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

Richard E. Lucas is an Associate Professor of Psychology at Michigan State University and a Research Professor of the German Socio-Economic Panel Study (GSOEP) at the German Institute for Economic Research (DIW, Berlin). He received his PhD in Psychology from the University of Illinois at Urbana-Champaign. His research focuses on the causes and consequences of subjective well-being. In particular, he studies the association between extraversion and positive affect, the functions of positive affect, the role of social activity and social relationships in well-being, and the extent to which people adapt to major life events and life circumstances including marriage, widowhood, divorce, unemployment, and chronic disability. Dr. Lucas is also interested in measurement and he conducts studies designed to evaluate the psychometric properties of personality and well-being measures. He has authored or co-authored papers on these topics for journals such as the Journal of Personality and Social Psychology, Psychological Bulletin, American Psychologist, Annual Review of Psychology, Psychological Science, Current Directions in Psychological Science, Psychological Assessment, and Perspectives on Psychological Science.

Footnotes

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References

* Correspondence address: Department of Psychology, Michigan State University, East Lansing, MI 48823, USA. Email: lucasri@msu.edu

1 It is important to note that this study is often cited because it is one of the first to demonstrate mood misattribution effects. We do not dispute the value of this paper in this regard. However, the study is also cited as showing that weather affects life satisfaction judgments, and it is this aspect that has apparently not been replicated. We also want to note that we believe that weather may in fact affect mood (e.g., Keller, Fredrickson, Ybarra, Cote, Johnson, Mikels, Conway, & Wager, 2005). But our point is that the carryover effect from weather to mood to life satisfaction judgments has not, to our knowledge, been replicated.

References

  1. Top of page
  2. Abstract
  3. Myth #1: SWB Measures Are Strongly Influenced by Transient (and Irrelevant) Factors
  4. Myth #2: The Small Correlation between Income and Happiness Means that the Rich Are Barely Happier than the Poor
  5. Myth #3: Social Relationships Have Been Shown to Be the Strongest Predictors of SWB
  6. Myth #4: People Adapt to All Life Circumstances and Happiness Cannot Change
  7. Summary
  8. Short Biography
  9. Footnotes
  10. References
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