Why? Because they realize happiness is a choice -- and the result of actions, not wishful thinking.
Everyone wants to be happy. Yet many people are not. Is that because of their circumstances, or because of their perspectives?
Great question. Approximately 50 percent of your level of happiness, or what psychologists call your "happiness set-point," is determined by personality traits that are largely hereditary. That means half of your level of happiness is largely outside your control.
That's too bad, but it also means that 50 percent of your level of happiness is largely within your control: health, career, relationships, activities, etc. So even if you were born with a tendency to be at least a little gloomy, you can still do things to make yourself a lot happier.
Like these:
Pursuing goals, though, does make you happy. According to David Niven, author of 100 Simple Secrets of the Best Half of Life, "People who could identify a goal they were pursuing were 19 percent more likely to feel satisfied with their lives and 26 percent more likely to feel positive about themselves."
So be grateful for what you have, and then actively try to achieve more. If you're pursuing a huge goal, make sure that every time you take a small step closer to achieving it, you pat yourself on the back.
But don't compare where you are now with where you someday hope to be. Compare where you are now to where you were a few days ago. Then you'll get dozens of bite-size chunks of fulfillment -- and a never-ending supply of things to be thankful for.
Why? I'm no researcher, but clearly the more you enjoy what you do and the more fulfilled you feel by it, the happier you will be.
In The Happiness Advantage, Shawn Achor says that when volunteers picked "one of their signature strengths and used it in a new way each day for a week, they became significantly happier and less depressed."
Of course it's unreasonable to think you can chuck it all and simply do what you love. But you can find ways to do more of what you excel at. Delegate. Outsource. Start to shift the products and services you provide into areas that allow you to bring more of your strengths to bear. If you're a great trainer, find ways to train more people. If you're a great salesperson, find ways to streamline your administrative tasks and get in front of more customers.
Everyone has at least a few things they do incredibly well. Find ways to do those things more often. You'll be a lot happier.
And probably a lot more successful.
But there's a definite payoff to making real (not just professional or social-media) friends. Increasing your number of friends correlates to higher subjective well-being; doubling your number of friends is like increasing your income by 50 percent in terms of how happy you feel.
And if that's not enough, people who don't have strong social relationships are 50 percent less likely to survive at any given time than those who do. (That's a scary thought for loners like me.)
Make friends outside of work. Make friends at work. Make friends everywhere.
Make real friends. You'll live a longer, happier life.
Of course the same is true at work. Express gratitude for employees' hard work, and you both feel better about yourselves.
Another easy method is to write down a few things you are grateful for every night. One study showed people who wrote down five things they were thankful for once a week were 25 percent happier after 10 weeks; in effect, they dramatically increased their chances of meeting their happiness set-point.
Happy people focus on what they have, not on what they don't have. It's motivating to want more in your career, relationships, bank account, etc., but thinking about what you already have, and expressing gratitude for it, will make you a lot happier.
It will also remind you that even if you still have huge dreams, you have already accomplished a lot -- and should feel genuinely proud.
Intuitively, I think we all know that, because it feels awesome to help someone who needs it. Not only is helping those in need fulfilling, it's a reminder of how comparatively fortunate we are -- which is a nice reminder of how thankful we should be for what we already have.
Plus, receiving is something you cannot control. If you need help -- or simply want help -- you can't make others help you. But you can always control whether you offer and provide help.
And that means you can always control, at least to a degree, how happy you are -- because giving makes you happier.
But after a certain point, money doesn't make people happier. After about $75,000 a year, money doesn't buy more (or less) happiness. "Beyond $75,000...higher income is neither the road to experience happiness nor the road to relief of unhappiness or stress," say two Princeton University researchers on the subject.
"Perhaps $75,000 is the threshold beyond which further increases in income no longer improve individuals' ability to do what matters most to their emotional well-being, such as spending time with people they like, avoiding pain and disease, and enjoying leisure," the researchers speculate.
And if you don't buy that, here's another take: "The materialistic drive and satisfaction with life are negatively related." Or, in layman's terms, "Chasing possessions tends to make you less happy."
Think of it as the bigger house syndrome. You want a bigger house. You need a bigger house. (Not really, but it sure feels like you do.) So you buy it. Life is good...for a couple months, until your bigger house is just your house.
The new always becomes the new normal.
"Things" provide only momentary bursts of happiness. To be happier, don't chase as many things. Chase a few experiences instead.
What other people think -- especially people you don't even know -- doesn't matter. What other people want you to do doesn't matter.
Your hopes, your dreams, your goals -- live your life your way. Surround yourself with people who support and care, not for the "you" they want you to be, but for the real you.
Make choices that are right for you. Say things you really want to say to the people who most need to hear them. Express your feelings. Stop and smell a few roses. Make friends, and stay in touch with them.
And most of all, realize that happiness is a choice. Fifty percent of how happy you are lies within your control, so start doing more things that will make you happier.
Addendum:
1. 100 Simple Secrets of the Best Half of Life: What Scientists Have Learned and How You Can Use It Paperback – April 5, 2005 ,
Everyone wants to be happy. Yet many people are not. Is that because of their circumstances, or because of their perspectives?
Great question. Approximately 50 percent of your level of happiness, or what psychologists call your "happiness set-point," is determined by personality traits that are largely hereditary. That means half of your level of happiness is largely outside your control.
That's too bad, but it also means that 50 percent of your level of happiness is largely within your control: health, career, relationships, activities, etc. So even if you were born with a tendency to be at least a little gloomy, you can still do things to make yourself a lot happier.
Like these:
1. Actively pursue your goals.
Goals you don't pursue aren't goals, they're dreams, and dreams make you happy only when you're dreaming.Pursuing goals, though, does make you happy. According to David Niven, author of 100 Simple Secrets of the Best Half of Life, "People who could identify a goal they were pursuing were 19 percent more likely to feel satisfied with their lives and 26 percent more likely to feel positive about themselves."
So be grateful for what you have, and then actively try to achieve more. If you're pursuing a huge goal, make sure that every time you take a small step closer to achieving it, you pat yourself on the back.
But don't compare where you are now with where you someday hope to be. Compare where you are now to where you were a few days ago. Then you'll get dozens of bite-size chunks of fulfillment -- and a never-ending supply of things to be thankful for.
2. Do what you do well, as often as you can.
You know the old cliché regarding the starving-yet-happy artist? Turns out it's true: Artists are considerably more satisfied with their work than non-artists -- even though the pay tends to be considerably lower than in other skilled fields.Why? I'm no researcher, but clearly the more you enjoy what you do and the more fulfilled you feel by it, the happier you will be.
In The Happiness Advantage, Shawn Achor says that when volunteers picked "one of their signature strengths and used it in a new way each day for a week, they became significantly happier and less depressed."
Of course it's unreasonable to think you can chuck it all and simply do what you love. But you can find ways to do more of what you excel at. Delegate. Outsource. Start to shift the products and services you provide into areas that allow you to bring more of your strengths to bear. If you're a great trainer, find ways to train more people. If you're a great salesperson, find ways to streamline your administrative tasks and get in front of more customers.
Everyone has at least a few things they do incredibly well. Find ways to do those things more often. You'll be a lot happier.
And probably a lot more successful.
3. Make good friends.
It's easy to focus on building a professional network of partners, customers, employees, connections, etc., because there is (hopefully) a payoff.But there's a definite payoff to making real (not just professional or social-media) friends. Increasing your number of friends correlates to higher subjective well-being; doubling your number of friends is like increasing your income by 50 percent in terms of how happy you feel.
And if that's not enough, people who don't have strong social relationships are 50 percent less likely to survive at any given time than those who do. (That's a scary thought for loners like me.)
Make friends outside of work. Make friends at work. Make friends everywhere.
Make real friends. You'll live a longer, happier life.
4. Actively express your thankfulness.
According to one study, couples who expressed gratitude in their interactions with each other experienced increased relationship connection and satisfaction the next day -- both for the person expressing thankfulness and (no big surprise) the person receiving it. (In fact, the authors of the study said gratitude was like a "booster shot" for relationships.)Of course the same is true at work. Express gratitude for employees' hard work, and you both feel better about yourselves.
Another easy method is to write down a few things you are grateful for every night. One study showed people who wrote down five things they were thankful for once a week were 25 percent happier after 10 weeks; in effect, they dramatically increased their chances of meeting their happiness set-point.
Happy people focus on what they have, not on what they don't have. It's motivating to want more in your career, relationships, bank account, etc., but thinking about what you already have, and expressing gratitude for it, will make you a lot happier.
It will also remind you that even if you still have huge dreams, you have already accomplished a lot -- and should feel genuinely proud.
5. Help other people.
While giving is usually considered unselfish, giving can also be more beneficial for the giver than the receiver: Providing social support may be more beneficial than receiving it.Intuitively, I think we all know that, because it feels awesome to help someone who needs it. Not only is helping those in need fulfilling, it's a reminder of how comparatively fortunate we are -- which is a nice reminder of how thankful we should be for what we already have.
Plus, receiving is something you cannot control. If you need help -- or simply want help -- you can't make others help you. But you can always control whether you offer and provide help.
And that means you can always control, at least to a degree, how happy you are -- because giving makes you happier.
6.Realize that more money won't make you happier.
Money is important. Money does a lot of things. (One of the most important is to create choices.)But after a certain point, money doesn't make people happier. After about $75,000 a year, money doesn't buy more (or less) happiness. "Beyond $75,000...higher income is neither the road to experience happiness nor the road to relief of unhappiness or stress," say two Princeton University researchers on the subject.
"Perhaps $75,000 is the threshold beyond which further increases in income no longer improve individuals' ability to do what matters most to their emotional well-being, such as spending time with people they like, avoiding pain and disease, and enjoying leisure," the researchers speculate.
And if you don't buy that, here's another take: "The materialistic drive and satisfaction with life are negatively related." Or, in layman's terms, "Chasing possessions tends to make you less happy."
Think of it as the bigger house syndrome. You want a bigger house. You need a bigger house. (Not really, but it sure feels like you do.) So you buy it. Life is good...for a couple months, until your bigger house is just your house.
The new always becomes the new normal.
"Things" provide only momentary bursts of happiness. To be happier, don't chase as many things. Chase a few experiences instead.
7. Live your life the way you want to live it.
Bonnie Ware worked in palliative care, spending time with patients who had only a few months to live. Their most common regret was, "I wish I'd had the courage to live a life true to myself, not the life others expected of me."What other people think -- especially people you don't even know -- doesn't matter. What other people want you to do doesn't matter.
Your hopes, your dreams, your goals -- live your life your way. Surround yourself with people who support and care, not for the "you" they want you to be, but for the real you.
Make choices that are right for you. Say things you really want to say to the people who most need to hear them. Express your feelings. Stop and smell a few roses. Make friends, and stay in touch with them.
And most of all, realize that happiness is a choice. Fifty percent of how happy you are lies within your control, so start doing more things that will make you happier.
Addendum:
1. 100 Simple Secrets of the Best Half of Life: What Scientists Have Learned and How You Can Use It Paperback – April 5, 2005 ,
Practical advice on how to thrive in the second half of your life, based on scientific studies. The sixth book in the bestselling 100 Simple Secrets series.
What do people who relish the second half of their lives do differently than those who dread getting older? Sociologists, therapists and psychiatrists have spent entire careers investigating the ins and outs of successful aging, yet their findings are inaccessible to ordinary people, hidden in obscure journals to be shared with other experts.
Now the international bestselling author of The 100 Simple Secrets series has collected the most current and significant data from more than a thousand of the best scientific studies on the second half of life. These findings have been boiled down to one hundred essential ways to find and maintain joy, health, and satisfaction every day of your life. Each one is accompanied by a true story showing the results in action.
The Baby Boomers are hitting retirement age. This upbeat, light approach will appeal to the enormous market of citizens grappling with the effects of becoming 'senior', looking to discover the positive benefits of aging beyond discount tickets at the movie theatre. Books about aging well continue to sell year in and year out. The Simple Secrets approach will stand out among the heavier self-help/psychology titles and will without a doubt become an affordable impulse and gifty mainstay in this category.
A good inexpensive gift for parents and grandparents.
To check it out, Look Inside, click Here.
2. The happy artist: an empirical application of the work-preference model.
How can the effects of these differing trends be compared? To judge the importance and value of differing forms of friendship requires a common basis for valuation. The broadening availability of data for subjective well-being offers one possible solution to this valuation problem. If it were possible to measure each individual’s network of on-line and real-life friends, then their respective contributions to subjective well-being could provide a way of comparing their values, and hence to judge whether the quality of social networks as a whole was growing or shrinking. Only very recently has there been a survey that provided comparable measures of networks of face-to-face and on-line friends, set in the context of a well-being survey of sufficient size and scope to permit comparable assessments of the two types of friends.
This is more than the equivalent of increasing household incomes by 150%. There is also a dose-response relationship, so that having more friends is better than having fewer. Evidence from the Canadian General Social Survey shows that, compared to respondents having no close friends, to have 3 to 5 close friends is associated with life satisfaction 0.24 points higher on a 10-point scale, an amount that rises to.32 for those with 6 to 10 close friends, and to 0.43 points for those with more than 20 close friends [3]. Also notable is that happiness depends not just on the number of close friends, but also the frequency with which they are seen [3], [4]. The same survey also asks about the number of close relatives, and the frequency with which they are seen. An interesting difference appears between friends and family. The number of close family matters more than the number of close friends, about twice as much up to 15 in number, with no gain thereafter, while frequency of seeing family contributes only half as much as the frequency of seeing close friends [3]. A similar result is found in US and other Canadian data analyzed by [5], where it is shown that the frequency of seeing friends adds twice as much to subjective well-being as does the frequency of seeing family. The US and Canadian surveys in [5] also reveal a strong relation between subjective well-being and the frequency of seeing friends, with those seeing friends most frequently having subjective well-being higher by 0.5 points on a ten-point scale.
All of these results are based on fully specified models with many other control variables, although there is no doubt likely to be some remaining element of mutual causality between subjective well-being and the frequency of seeing friends. For example, those who are at the bottom end of the subjective well-being scale, and especially those who are clinically depressed, often reduce the extent to which they reach out to friends. Indeed social withdrawal is a key element in the Beck Depression Inventory (BDI) [6], as supported in subsequent factor-analytic work by [7]. Thus some of the strong positive linkages between friends and happiness may reflect causal influences running in both directions. This is likely to apply for both real-life and on-line friends, and hence should not affect our comparisons in this paper between these two types of friends.
There are few studies of the linkages between on-line friendships and subjective well-being. One study [8] found a positive relation between subjective well-being and number of Facebook friends among a sample of 391 college-age subjects. Another study of college-age respondents in the United States, while not directly investigating the links between Facebook usage and subjective well-being, did find evidence that Facebook usage was correlated with proxy measures of social capital, but only for those with relatively low levels of satisfaction with campus life [9]. An earlier study of social capital and internet usage in a sample of US adolescents [10] found no significant relation between subjective well-being and time spent on-line. Those who spent more time messaging with close real-life friends were happier. Conversely, the relation between on-line time and subjective well-being was negative for those in contact with strangers or purely on-line friends. A recent study of Egyptian students found no significant relation between life satisfaction and intensity of Facebook usage [11].
Although there are many studies showing the effects of marital status on subjective well-being, we have not found previous attempts to see if the happiness effects of either real-life or on-line friends differ by marital status. Using two different surveys, we look for, and find, a large interaction effect in the happiness effects of marital status and real-life friends, but no significant differences for the effects of on-line friends.
We think that our results are the first to compare the happiness effects of real-life and on-line friends. Hence there are no directly comparable prior studies. Based on a meta-analysis [12] of fifty years of studies showing significantly more effective cooperation in conflict resolutions using face-to-face rather than written communications, we might conjecture that a similar difference might exist to differentiate the happiness effects of real-life and on-line friends.
From our perspective, the most interesting questions (other than the ones on well-being) are those on the size of social networks, separately for real-life friends and on-line friends. This presents an opportunity for us to examine potential differences between these two types of networks, specifically in their contributions to subjective well-being.
We use regression analysis to relate measures of subjective well-being to the sizes of social networks, as well as income and demographic controls. We will also use control variables to pick up differences in personalities; such variables include self-reported stress, time spent exercising and contributions to charitable causes.
The survey’s primary measure of subjective well-being is an 11-point (from 0 to 10) life ladder, based on the question “Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” This question, also known as Cantril’s Self-Anchoring Ladder, is frequently used in well-being studies, including the recent World Happiness Report [13] and many studies cited therein. We plot the distribution of sample responses in the first panel of Figure 1. The mode is “7” with a quarter of the respondents. The next greatest concentration is “8” with about 20% of the responses. The sample mean is 6.8, significantly lower than for the Canadian ladder responses in the Gallup World Poll, as shown in figure 2.3 of the World Happiness Report.
It is possible to construct two other measures of well-being from the survey. One is life satisfaction, based on the four-point responses to the question “To what extent do you agree with each of the following statements” that include a statement “I am satisfied with my life”. The four points are “strongly agree”, “somewhat agree”, “somewhat disagree” and “strongly disagree”. The second panel of Figure 1 shows the distribution. The mode, covering more than 50% of the responses, is “somewhat agree”. Another potential measure is the response to the question “How happy are you at the beginning of 2011? Very happy, somewhat happy, somewhat unhappy, very unhappy.” The distribution of happiness is similar to that of life satisfaction: the third step “somewhat happy” has more than 50% of the sample. We will use these two measures of well-being for robustness tests.
There is also a question on the level of stress, specifically the response to the question “How would you rate your average daily stress levels? Very low, Low, Medium, High, Very high.” Its distribution is shown in the last panel of Figure 1. The response of “Medium” has the greatest share of responses at 40%.
We now move on to the two questions on social networks. The first question concerns real-life friends. The exact wording is “How big is your real-life social network of friends?” The permitted responses, unless the respondents refuse to answer, include “Less than 10 friends”, “Between 10 and 20 friends”, “Between 20 and 30 friends”, “Between 30 and 50 friends”, and “More than 50 friends”. The distribution of the network size is shown in the upper panel of Figure 2. A large majority of the sample, almost 80%, is in the first two categories (i.e., with fewer than 20 friends).
The immediately next question in the survey concerns online friends: “How big is your online social network?” The responses include “I don't have an online social network”, “Less than 100”, “Between 100–300”, “Between 300–500”, “Between 500–700” and -Greater than 700”. The distribution is shown on the lower panel of Figure 2. A large majority of the sample either has no online friends (about 25%) or have fewer than 100 of them (about 50%).
The two network questions have different numbers of steps, and both have some steps with sparse responses (see Figure 2). We correct for these problems by combining the top two categories of real-life network into one single category with 11% of the sample, and the top three categories of online network into one category with 9% of the sample. This way, we turn the two network sizes into a comparable scale of four steps. In the case of real-life network, the four categories are “less than 10”, “10–20”, “20–30” and “30 or more”, with 44%, 34% 11% and 11% of the sample, respectively. The size of online network falls into “0”, “1–100”, “100–300”, “300 or more”, with 23%, 50.8%, 17.6% and 8.6% of the sample, respectively.
Table 1 presents summary statistics of other variables. The average age is 45. Forty-five percent (45%) of the sample are married; 15% in common-law relation, 5% dating, 23% single; the remaining 12% are divorced, separated, widowed or are unknown. The income information is based on categorical responses of income intervals. We estimate the midpoint of each interval under the assumption that income follows a log-normal distribution. We then assign respondents in each interval the corresponding midpoint estimate. The categories for the income variables are “$20,000 and below”, “$20,001 to $35,000”, “$35,001 to $50,000”, “$50,001 to $75,000”, “$75,001 and $110,000” and “more than $110,000”. The estimated midpoints are $13,605, $27,073, $41,895, $60,345, $87,895 and $136,849 respectively. About 15% of the sample did not provide income information. We use a dummy variable to indicate such a status in the regression analysis. Among those that have valid income information, the sample mean is $51 thousand. The average time spent on moderate to high intensity exercising is 1.78 hours per week. Close to 60% of survey respondents indicated that they currently volunteer or give time or money to charitable causes.
5. Practicing Gratitude Can Increase Happiness by 25%
Psychological research finds that people’s happiness levels are remarkably stable over the long-term. Whether you win the lottery or are paralysed from the neck down, after about three to six months you’ll have returned to your usual level of happiness. While these findings are deeply counter-intuitive, they also raise a serious problem for those wanting to increase levels of happiness permanently.
A possible answer comes from recent research in the psychology of gratitude. Yes, you read that correctly – being thankful might be the key to raising your happiness ‘set-point’. And there is some good experimental evidence to back up this theory.
‘, Dr. Robert A. Emmons describes research he carried out with three experimental groups over 10 weeks (Emmons & McCullough, 2003):
experience and more articles.
All this from reflecting on the pleasure of having seen the sunset through the clouds? Dr Emmons also expresses surprise at the findings of the study, partly because there are some reasons practising gratitude might not be so good.
For example, focussing on gratitude reminds us what we owe to others. This may in turn remind us of our dependence on others and reduce a sense of personal control. Thinking in terms of gratitude may also focus us on the debts we owe to others and, studies have shown, people don’t enjoy feeling indebted to others.
In a second study, very similar to the first described above, Emmons and McCullough changed one of the control conditions. Instead of asking people to write down any events from the week, people were asked to list ways in which they were better off than others. The idea was that in this condition people are making positive comparisons but are not necessarily thinking gratefully (although it can’t be ruled out!).
Again, though, the results showed that those in the gratitude condition were significantly happier than those making positive comparisons between themselves and others. Unsurprisingly those practising being grateful were also happier than those focussing on daily hassles.
In a third study Emmons and McCullough recruited adults who had neuromuscular disorders, often as a delayed result of surviving infection by the polio virus. While not life-threatening the condition can be seriously debilitating, causing joint and muscle pain as well as muscle atrophy. People with this condition have a good reason to be dissatisfied with the hand life has dealt them.
In this study a gratitude condition was compared to a control condition in which participants wrote about their daily experience. After the 21 day study, participants in the gratitude condition were found to be more satisfied with their lives overall, more optimistic about the upcoming week and crucially, were sleeping better. Good sleep is important as it has been found to be a great indicator of overall well-being. People who sleep well are generally healthier and happier than those whose sleep is poor.
6. PROVIDING SOCIAL SUPPORT MAY BE MORE BENEFICIAL THAN RECEIVING IT:
Results From a Prospective Study of Mortality
Conceptually, it is not clear that receiving social support will always be beneficial. For example, depending on other people for support can cause guilt and anxiety (Lu & Argyle, 1992). And feeling like a burden to others who presumably provide support is associated with increased suicidal tendencies, even after controlling for depression (R.M. Brown, Dahlen, Mills, Rick, & Biblarz, 1999; de Catanzaro, 1986). The correlation of social support with dependence may help to explain why studies have failed to consistently confirm the social-support hypothesis. (source click here)
Furthermore, the benefits of social contact may extend beyond received support to include other aspects of the interpersonal relationship that may protect health and increase longevity—for example, giving support to others. However, with few exceptions (e.g., Liang, Krause, & Bennett, 2001), social-support studies rarely assess whether there are benefits from providing support to others. Some measures of social support do seem to tap giving—perhaps inadvertently—yet the benefits are often attributed to receiving support or sometimes attributed to reciprocated support. For example, a nationwide survey of older peoples' support networks measured social support by a combination of what was received and what was provided to others (Antonucci, 1985). Implicit in this assessment is the recognition that receiving social support is likely to be correlated with other aspects of close relationships, including the extent to which individuals give to one another. Thus, some of the benefits of social contact, traditionally attributed to receiving support, or to reciprocated support (e.g., Antonucci, Fuhrer, & Jackson, 1991), may instead be due to the benefits of giving support.
THE BENEFITS OF PROVIDING SUPPORT TO OTHERS
There are both theoretical and empirical reasons to hypothesize that giving support may promote longevity. For example, kin-selection theory (Hamilton, 1964a, 1964b) and reciprocal-altruism theory (Trivers, 1971) suggest that human reproductive success was contingent upon the ability to give resources to relationship partners. Social bonds (S.L. Brown, 1999) and emotional commitment (Nesse, 2001) have been theorized to promote high-cost giving. The resulting contribution made to relationship partners is theorized to trigger a desire for self-preservation on the part of the giver, enabling prolonged investment in kin (de Catanzaro, 1986) and reciprocal altruists.
Although few studies have explicitly examined whether helping others increases longevity, sociologists note the ubiquity of giving to others (Rossi, 2001), and studies show that individuals derive benefits from helping others, such as reduced distress (Cialdini, Darby, & Vincent, 1973; Midlarsky, 1991) and improved health (Schwartz & Sendor, 2000). Moreover, volunteering has beneficial effects for volunteers, including improved physical and mental health (Omoto & Synder, 1995; Wilson & Musick, 1999). Even perceptions that are likely to be associated with giving, such as a sense of meaning, purpose, belonging, and mattering, have been shown to increase happiness and decrease depression (e.g., Taylor & Turner, 2000; see Batson, 1998, for a review).
THE PRESENT STUDY
Using data from the Changing Lives of Older Couples (CLOC) sample, we addressed two questions: (a) Do the benefits of providing social support account for some or all of the benefits of social contact that are traditionally interpreted as due to support received from others? (b) Does receiving support influence mortality once giving support and dependence are controlled?
Traditionally, social support has been defined in numerous ways, leading some authors to conclude that measurement issues are a source of contradictory findings (e.g., Smerglia, Miller, & Kort-Butler, 1999). For the purpose of the present study, we focused our analyses on items for which our measures of giving and receiving tapped similar domains of support. Similar domains of support were measured for the exchange of emotional support between spouses and the exchange of instrumental support with individuals other than one’s spouse. House (1981) suggested that these two domains of support—emotional and instrumental— represent two of the four functions of interpersonal transactions.
To isolate the unique effects of giving and receiving social support on mortality, it was important to control for factors that may influence any of these variables, including age, gender, perceived health, health behaviors, mental health, socioeconomic status, and some individual difference variables (personality traits). Controlling for these variables helped to increase our confidence that any beneficial effect of giving we observed was not due to enhanced mental or physical robustness of the giver. We also examined variables associated with relationship phenomena that could influence giving support, receiving support, and dependence; these variables included perceived equity (the perception that one receives the same amount as one provides to the relationship partner) and relationship satisfaction. Responses at baseline were used to predict mortality status over the ensuing 5-year period of the study.
METHOD
Sample
The CLOC study is a prospective study of a two-stage area probability sample of 1,532 married individuals from the Detroit Standard Metropolitan Statistical Area. The husband in each household was 65 years of age or older (see Carr et al., 2000, for a complete report). Of those individuals who were selected for participation in the CLOC study, 65% agreed to participate, a response rate consistent with response rates in other studies in the Detroit area (Carr et al., 2000). More than one half of the sample (n = 846) consisted of married couples for whom mortality data on both members was available. These 423 married couples were the respondents in the present study. 1 Baseline measures were administered in face-to-face interviews, conducted over an 11-month period in 1987 and 1988. Of the subsample of 846 respondents, 134 died over the 5-year course of the study.
Mortality Data
Mortality was monitored over a 5-year period by checking daily obituaries in three Detroit-area newspapers and monthly death-record tapes provided by the State of Michigan. Mortality status was indicated with a dichotomous variable (1 = deceased, 0 = alive).
Baseline Measures
Instrumental support
Giving instrumental support to others, GISO, was measured by four survey questions that asked respondents whether they had given instrumental support to friends, neighbors, and relatives other than their spouse in the past 12 months. Respondents indicated (yes/no) whether they helped with (a) transportation, errands, shopping; (b) housework, (c) child care, and (d) other tasks. Respondents were instructed to say “yes” to any of these questions only if they did not live in the same household with the recipient of support and they did not receive monetary compensation. Responses were coded so that a “0” indicated a “no” response to all four items, and a “1” indicated a “yes” response to at least one item.
Receiving instrumental support from others, RISO, was assessed by a single item: “If you and your husband [wife] needed extra help with general housework or home maintenance, how much could you count on friends or family members to help you?” Responses were coded on a 4-point scale. [1]
Emotional support
Giving and receiving emotional support was assessed with items from the Dyadic Adjustment Scale (Spanier, 1976). Giving emotional support to a spouse, GESS, was assessed using two items that asked participants whether they made their spouse feel loved and cared for and whether they were willing to listen if their spouse needed to talk (a = .51). Rankin-Esquer, Deeter, and Taylor (2000) reviewed evidence to suggest that the benefits of receiving emotional support from a spouse come from both feeling emotionally supported by a spouse and feeling free to have an open discussion with one’s spouse. The two-item measure of receiving emotional support from a spouse, RESS, (a = .66) was identical with the exception that participants were asked whether their spouse made them feel loved and cared for, and whether their spouse was willing to listen if they needed to talk. Responses were coded on a 5-point scale. [2]
Control variables
To control for the possibility that any beneficial effects of giving support are due to a type of mental or physical robustness that underlies both giving and mortality risk, we measured a variety of demographic, health, and individual difference variables. (See Appendix A for a description of the health, mental health, and personality variables used.) Both age and gender (1 = male, 2 = female) were controlled for in each analysis to take into account the possibilities that (a) older people give less and are more likely to die than younger people and (b) females give more and are less likely to die than males.
To isolate the unique effects of giving and receiving support, above and beyond other known relationship influences on health, we included measures of social contact and dependence. Social contact was assessed with the mean of the following three questions: “In a typical week, about how many times do you talk on the phone with friends, neighbors, or relatives?” “How often do you get together with friends, neighbors, or relatives and do things like go out together or visit in each other’s homes?” and “How often do you go out socially, by yourself, or with people other than your husband [wife]?” Scores were standardized so that higher values indicated greater social contact (a = .51).
Dependence on the spouse was coded on a 4-point scale and was measured with three items asking participants whether losing their spouse would make them feel lost, be terrifying, or be the worst thing that could happen to them (a = .82).
Additional relationship variables
We measured additional aspects of the marital relationship in order to examine alternative explanations for any effects of giving and receiving emotional support. Specifically, we used items from the Dyadic Adjustment Scale (Spanier, 1976) to assess equity (the absolute value of the difference between an individuals' ratings of perceived emotional support received from the partner and perceived emotional support provided to the partner; higher values indicated greater discrepancy) and marital satisfaction (one item).
Additional measures of receiving and giving support
To consider the possibility that any observed benefits of giving or receiving support were an artifact of the chosen measures, we included all of the remaining support measures from the CLOC data set (Appendix B).
RESULTS
We examined our hypotheses using the 846 persons for whom mortality data were available. Because this sample included the responses of both members of a couple, we computed the intraclass correlation (ICC) for the couple-level effect on mortality. We first created a variable that grouped individual participants by couple (n = 423). We next constructed a two-level hierarchical model (Level 1 estimated variation in mortality at the individual-participant level, Level 2 estimated variation at the couple level) using RIGLS (restricted iterative generalized least squares) estimation for binomial models (MLwiN ver. 1.1, Multilevel Models Project, Institute of Education, London, 2000). A significant ICC could be interpreted as indicating that the death of one partner was significantly related to an increase or decrease in the probability of the other partner dying (within the study period). Results of this procedure indicated that there was no couple-level effect on mortality (ICC = .00, n.s.). Thus, for all analyses, we treated each member of a couple as an independent source of data.
Giving Support, Receiving Support, and Social Contact
Table 1 presents a correlation matrix of the focal social-support measures. Receiving and giving were significantly and strongly correlated for measures of emotional support exchanged between spouses (r = .58, p < .001), and weakly correlated for measures of instrumental support exchanged with others (r = .09, p < .01).
To examine whether giving instrumental support reduced risk of mortality, we ran a hierarchical logistic regression procedure. Results of this analysis are displayed in Fig. 1, and the odds ratios are presented in Table 2. Step 1 of this analysis regressed mortality status on social contact, age, and gender. The results were consistent with previous research in indicating that social contact reduced the risk of mortality (b = -0.21, p < .05). To examine whether giving versus receiving support accounted for this effect, we entered GISO and RISO simultaneously in the second step. Results at this step indicated that mortality risk was decreased by GISO (b = -0.85, p < .001) but increased by RISO (b = 0.17, p < .10). Social contact was no longer significant at this step (b = -0.13, n.s.).
Because individuals in poor health may have difficulty providing others with instrumental support, functional health status, satisfaction with health, health behaviors, and mental health variables were added to the model in order to control for the alternative possibility that individuals who give support to others live longer because they are more mentally and physically robust than those who do not give support.
Results at this step indicated that after controlling for these measures of health, the effect of GISO was reduced, but GISO was still significantly related to mortality (b = -0.56, p < .01). In fact, GISO exerted a beneficial effect on mortality even after controlling for interviewer ratings of health, income and education level, self-reports of feeling vulnerable to stress, dispositional influences on mortality, and personality influences on mortality. After all control variables were held constant, GISO significantly decreased mortality risk (b = -0.54, p < .05), and RISO marginally increased mortality risk (b = 0.23, p < .10).
These results support the hypothesis that giving support accounts for some of the benefits of social contact. However, our findings are based on the use of different measures to operationalize giving and receiving support. That is, the GISO variable measured support that was actually provided to other people (i.e., enacted support), whereas the RISO variable assessed whether others could be depended upon to provide support (i.e., available support).4 Furthermore, it is not clear whether the adverse effect of RISO was due to received support or to the covariation of received support with dependence. In order to control for the difference between the giving and receiving measures, as well as the potentially adverse effect of dependence, we examined the exchange of emotional support between spouses. This domain of support offered virtually identical giving and receiving measures, and included measures of dependence.
Analyses With Identical Measures of Giving and Receiving Support
To clarify the role of receiving support on mortality, we ran a hierarchical logistic regression procedure in which RESS was entered in Step 1, along with age and gender. As can be seen in Figure 2, there was no significant effect of RESS on the risk of mortality (b = -0.17, n.s.). However, after controlling for the effect of dependence in Step 2, the effect of RESS became a significant predictor of reduced mortality risk (b = -0.23, p < .05). Thus, the results of Step 2 replicated the typical beneficial effect of receiving support found in the literature—but only after the adverse effect of dependence was held constant.
To compare the relative benefits of receiving versus giving support using identical measures, we entered GESS on the third step of this analysis. As shown in Figure 2, the unique effect of GESS accounted for a significant decrease in mortality risk (b = -0.36, p < .05), and rendered the effect of RESS nonsignificant (b = -0.05, n.s.). In order to examine whether GESS remained beneficial after controlling for GISO and the cumulative effect of all of the control variables, we entered GESS into the hierarchical regression model presented in Table 2 (Step 5). Results of this analysis demonstrated that both GESS (b = -0.51, p < .01) and GISO (b = -0.50, p < .05) made a unique, significant contribution to reducing mortality risk, above and beyond that of the control variables. Thus giving to one’s spouse (GESS) and giving to friends, relatives, and neighbors (GISO) both appear to exert an independent influence on the reduction in risk of mortality.
Finally, we examined two additional relationship factors that may be related to giving support--equity and marital satisfaction. We first added marital satisfaction to the overall model (shown in Table 2 and Fig. 1); it was not a significant predictor of mortality (b = -0.15, n.s.), nor did it affect the strength of any of the other predictors. We ran a similar model for equity, without GESS and RESS. Equity did not predict mortality (b = 0.20, n.s.).
Additional Measures of Receiving and Giving
Because the CLOC data included additional measures of giving and receiving, it was possible to determine whether our pattern of results was simply an artifact of the measures chosen. To examine this possibility, we correlated mortality status with each of the giving and receiving measures available in the CLOC data set. In addition, the composites for giving support were broken down into single items and correlated independently with mortality status. As shown in Table 3, only 1 of the 10 different receiving measures significantly reduced mortality risk5; 1 receiving measure significantly increased mortality risk. In contrast, all 4 of the different giving measures significantly reduced mortality risk. When the composites for giving support were broken down, 4 of the 6 items were significantly correlated with decreased mortality risk, including the only item that assessed available, rather than enacted, support. Taken together, these findings strongly suggest that giving support, rather than receiving support, accounts for the benefits of social contact, across different domains of support, different targets of support, and different structural features of support.
DISCUSSION
In this study, older adults who reported giving support to others had a reduced risk of mortality. The provision of support was correlated with reduced mortality in all analyses, whether giving support was operationalized as instrumental support provided to neighbors, friends, and relatives or as emotional support provided to a spouse. It is important to note that our analyses controlled for a wide range of demographic, personality, and health variables that might have accounted for these findings. Thus, these results add to a small but growing body of research that documents the health benefits of providing support to others (McClellan, Stanwyck, & Anson, 1993; Midlarsky, 1991; Schwartz & Sendor, 2000).
We also found that the relationship between receiving social support and mortality changed as a function of whether dependence and giving support were taken into consideration. Receiving emotional support (RESS) appeared to reduce the risk of mortality when dependence but not giving emotional support was controlled. Receiving instrumental support from others appeared to increase the risk of mortality when giving support, but not dependence, was controlled. Taken together, these findings may help to explain why tests of the social-support hypothesis have produced contradictory results. If the benefits of social contact are mostly associated with giving, then measures that assess receiving alone may be imprecise, producing equivocal results.
Although we have identified no single mediator of the link between giving support and mortality—one that could be informative about the process underlying the beneficial effects of giving support—many social psychological studies show that helping others increases positive emotion (e.g., Cialdini & Kenrick, 1976). Positive emotions, in turn, have been demonstrated to speed the cardiovascular recovery from the aftereffects of negative emotion (Fredrickson, Mancuso, Branigan, & Tugade, 2000). Thus, helping may promote health through its association with factors, such as positive emotion, that reduce the deleterious effects of negative emotion. Research is currently under way to examine this possibility.
More broadly, our results are consistent with the possibility that the benefits of social contact are shaped, in part, by the evolutionary advantages of helping others. The effects of giving support accounted for the benefits of both social contact and receiving support. In an evolutionary context, this result can be interpreted as indicating that older adults may still be able to increase their inclusive fitness (the reproductive success of individuals who share their genes) by staying alive and prolonging the amount of time they can contribute to family members (de Catanzaro, 1986). Of course this possibility relies on the assumption that a motivation for self-preservation can influence mortality. In fact, there is evidence to suggest that individuals with a “fighting spirit” survive longer with cancer than individuals who feel helpless or less optimistic about their chance of survival (Greer, Morris, & Pettingale, 1994).
Limitations and Directions for Future Research
Although the prospective, longitudinal design of this study is very strong, given the outcome of interest, alternative explanations for these findings remain viable. It may be, for example, that giving support is a better measure of health than receiving support, or that individuals who have the resources and motivation to give are also more robust than those who do not, or that an abundance of resources promotes longevity and makes it easier to give. We attempted to control for these alternative possibilities by demonstrating that the beneficial effects of giving support are above and beyond the effects of age, functional health, satisfaction with health, health behaviors, mental health, interviewer ratings of health, socioeconomic status, and vulnerability to stress. Moreover, two distinct types of giving–GESS and GISO–contributed simultaneously to longevity. This means that a third variable correlated with one measure of giving—such as robustness of one’s health—would have been held constant in a model that simultaneously tested the effect of the other giving measure. Thus, it is unlikely that the same alternative explanation can account for both effects of giving support. Of course, given the correlational nature of the study design, the regression methods used to disentangle these alternatives do not give the confidence that would be achieved by an experimental design. Nonetheless, longitudinal prospective studies like the one described here are important precursors to eventual long-term (and large-scale) experimental interventions that promote giving support.
It would be premature, on the basis of a single study, to conclude that giving support accounts for the traditional effects of receiving social support found in the literature (to our knowledge, no other studies have advanced this hypothesis). Nevertheless, the results of the present study should be considered a strong argument for the inclusion of measures of giving support in future studies of social support. Perhaps more important, our results corroborate the suggestion by House and his colleagues (1988) that researchers should be cautious of assuming that the benefits of social contact reside in the supportive quality of the relationship. Thus, whether or not mortality risk is a function of giving support, our results highlight the continued need for further research to seriously examine the fundamental assumption guiding the study of social support.
Conclusion
Giving support may be an important component of interpersonal relationships that has considerable value to health and well-being. It may not be a coincidence that mortality and morbidity studies inadvertently assess giving or manipulate giving (e.g., taking care of a plant; Rodin & Langer, 1977) to operationalize variables of interest such as receiving social support and locus of control. If giving, rather than receiving, promotes longevity, then interventions that are currently designed to help people feel supported may need to be redesigned so that the emphasis is on what people do to help others. The possibility that giving support accounts for some of the benefits of social contact is a new question that awaits future research. (source click here)
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2. The happy artist: an empirical application of the work-preference model.
Abstract
The artistic labor market is marked by several adversities, such as low wages, above-average unemployment, and constrained underemployment. Nevertheless, it attracts many young people. The number of students exceeds the available jobs by far. A potential explanation for this puzzle is that artistic work might result in exceptionally high job satisfaction, a conjecture that has been mentioned at various times in the literature. We conduct the first direct empirical investigation into artists’ job satisfaction. The analysis is based on panel data from the German Socio-Economic Panel Survey. Artists on average are found to be considerably more satisfied with their work than non-artists, a finding that corroborates the conjectures in the literature. Differences in income, working hours, and personality cannot account for the observed difference in job satisfaction. Partially, but not fully, the higher job satisfaction can be attributed to the higher self-employment rate among artists. Suggestive evidence is found that superior “procedural” characteristics of artistic work, such as increased variety and on-the-job learning, contribute to the difference in job satisfaction.
Table 6
Occupations included in the two definitions of artists
Performing & visual artists
|
Performing artists
| |
|---|---|---|
Authors, journalists, other writers (2,451)
|
Yes
| |
Sculptors, painters, related artists (2,452)
|
Yes
| |
Composers, musicians, singers (2,453)
|
Yes
|
Yes
|
Choreographers, dancers (2,454)
|
Yes
|
Yes
|
Film, stage and related actors, directors (2,455)
|
Yes
|
Yes
|
Photographers, image and sound recording equipment operators (3,131)
|
Yes
| |
Street, night-club and related musicians, singers, dancers (3,473)
|
Yes
|
Yes
|
Clowns, magicians, acrobats, related professionals (3,474)
|
Yes
|
Yes
|
Table 7
Variables
Name
|
Description
|
|---|---|
Job satisfaction
|
Overall job satisfaction. Scale: 0 (totally unsatisfied) to 10 (totally satisfied)
|
Performing & Visual Artists
|
=1 if individual is a performing or visual artist in her principal occupation, 0 else (see Table 6 for a list of the respective occupations)
|
Performing Artists
|
=1 if individual is a performing artist in her principal occupation, 0 else (see Table 6 for a list of the respective occupations)
|
Total gross income
|
Current gross monthly labor income in Euros
|
Working hrs. per week
|
Total working hours in an average week (including overtime)
|
Tenure
|
Firm tenure in years
|
Self-employed
|
=1 if self-employed, 0 if employed
|
Age
|
Age in years
|
Sex
|
=1 if female
|
Years of education
|
Amount of education (or training) in years
|
East
|
=1 if living in one of the new Laender, 0 if living in one of the old Laender
|
Foreign
|
=1 if nationality is not German
|
3. Comparing the Happiness Effects of Real and On-Line Friends
Abstract
A recent large Canadian survey permits us to compare face-to-face (‘real-life’) and on-line social networks as sources of subjective well-being. The sample of 5,000 is drawn randomly from an on-line pool of respondents, a group well placed to have and value on-line friendships. We find three key results. First, the number of real-life friends is positively correlated with subjective well-being (SWB) even after controlling for income, demographic variables and personality differences. Doubling the number of friends in real life has an equivalent effect on well-being as a 50% increase in income. Second, the size of online networks is largely uncorrelated with subjective well-being. Third, we find that real-life friends are much more important for people who are single, divorced, separated or widowed than they are for people who are married or living with a partner. Findings from large international surveys (the European Social Surveys 2002–2008) are used to confirm the importance of real-life social networks to SWB; they also indicate a significantly smaller value of social networks to married or partnered couples.Introduction
There are constant changes in the types of activities that people engage in, and in the technologies they use to establish and enjoy their social connections. For example, Robert Putnam’s analysis of movements in social capital in the United States over the 20th century showed that memberships in most US organizations, the frequency of dinner parties, league bowling, and many other types of social connection grew for the first 70 years of the 20th century and declined thereafter [1]. Some commentators and researchers argued that there were new types of social connection, possibly more effective in nature, that were growing and possibly offsetting the effects of declines elsewhere. One of the key examples offered was the substitution of on-line for face-to-face (we use this term interchangeably with ‘real-life’) friendships. The internet could thereby be seen as providing ways of enhancing or replacing face-to-face friends through the availability of on-line social networks.How can the effects of these differing trends be compared? To judge the importance and value of differing forms of friendship requires a common basis for valuation. The broadening availability of data for subjective well-being offers one possible solution to this valuation problem. If it were possible to measure each individual’s network of on-line and real-life friends, then their respective contributions to subjective well-being could provide a way of comparing their values, and hence to judge whether the quality of social networks as a whole was growing or shrinking. Only very recently has there been a survey that provided comparable measures of networks of face-to-face and on-line friends, set in the context of a well-being survey of sufficient size and scope to permit comparable assessments of the two types of friends.
Literature Review
Friends and family are a long-established support for subjective well-being. Friends matter to happiness both for being potential sources of social support and for the pleasures from time spent together, whether at work, at play, or in activities for the benefit of others. Data from the Gallup World Poll suggest that having someone to call on in times of trouble is associated with a life evaluation that is higher, on a 0 to 10 scale, by almost half a point (page 298 in [2]).This is more than the equivalent of increasing household incomes by 150%. There is also a dose-response relationship, so that having more friends is better than having fewer. Evidence from the Canadian General Social Survey shows that, compared to respondents having no close friends, to have 3 to 5 close friends is associated with life satisfaction 0.24 points higher on a 10-point scale, an amount that rises to.32 for those with 6 to 10 close friends, and to 0.43 points for those with more than 20 close friends [3]. Also notable is that happiness depends not just on the number of close friends, but also the frequency with which they are seen [3], [4]. The same survey also asks about the number of close relatives, and the frequency with which they are seen. An interesting difference appears between friends and family. The number of close family matters more than the number of close friends, about twice as much up to 15 in number, with no gain thereafter, while frequency of seeing family contributes only half as much as the frequency of seeing close friends [3]. A similar result is found in US and other Canadian data analyzed by [5], where it is shown that the frequency of seeing friends adds twice as much to subjective well-being as does the frequency of seeing family. The US and Canadian surveys in [5] also reveal a strong relation between subjective well-being and the frequency of seeing friends, with those seeing friends most frequently having subjective well-being higher by 0.5 points on a ten-point scale.
All of these results are based on fully specified models with many other control variables, although there is no doubt likely to be some remaining element of mutual causality between subjective well-being and the frequency of seeing friends. For example, those who are at the bottom end of the subjective well-being scale, and especially those who are clinically depressed, often reduce the extent to which they reach out to friends. Indeed social withdrawal is a key element in the Beck Depression Inventory (BDI) [6], as supported in subsequent factor-analytic work by [7]. Thus some of the strong positive linkages between friends and happiness may reflect causal influences running in both directions. This is likely to apply for both real-life and on-line friends, and hence should not affect our comparisons in this paper between these two types of friends.
There are few studies of the linkages between on-line friendships and subjective well-being. One study [8] found a positive relation between subjective well-being and number of Facebook friends among a sample of 391 college-age subjects. Another study of college-age respondents in the United States, while not directly investigating the links between Facebook usage and subjective well-being, did find evidence that Facebook usage was correlated with proxy measures of social capital, but only for those with relatively low levels of satisfaction with campus life [9]. An earlier study of social capital and internet usage in a sample of US adolescents [10] found no significant relation between subjective well-being and time spent on-line. Those who spent more time messaging with close real-life friends were happier. Conversely, the relation between on-line time and subjective well-being was negative for those in contact with strangers or purely on-line friends. A recent study of Egyptian students found no significant relation between life satisfaction and intensity of Facebook usage [11].
Although there are many studies showing the effects of marital status on subjective well-being, we have not found previous attempts to see if the happiness effects of either real-life or on-line friends differ by marital status. Using two different surveys, we look for, and find, a large interaction effect in the happiness effects of marital status and real-life friends, but no significant differences for the effects of on-line friends.
We think that our results are the first to compare the happiness effects of real-life and on-line friends. Hence there are no directly comparable prior studies. Based on a meta-analysis [12] of fifty years of studies showing significantly more effective cooperation in conflict resolutions using face-to-face rather than written communications, we might conjecture that a similar difference might exist to differentiate the happiness effects of real-life and on-line friends.
Data and Summary Statistics
The primary dataset for the paper is the 2011 Happiness Monitor survey sponsored by Coca-Cola and conducted in Canada between January 20 and 31, 2011 by Leger Marketing, using their online panel LegerWeb. The sample includes 5,025 Canadian residents, aged 16 and over, drawn from all ten Canadian provinces. The survey focuses on subjective well-being, and has questions that cover self-evaluation of life and other questions that can be used to construct alternative measures of well-being. It also has questions on people’s opinions about how various elements in life contribute to happiness. A section called Canadiana has occasionally light-hearted questions such as what is the happiest job in Canada, with a list that includes Zamboni driver and lumberjack.From our perspective, the most interesting questions (other than the ones on well-being) are those on the size of social networks, separately for real-life friends and on-line friends. This presents an opportunity for us to examine potential differences between these two types of networks, specifically in their contributions to subjective well-being.
We use regression analysis to relate measures of subjective well-being to the sizes of social networks, as well as income and demographic controls. We will also use control variables to pick up differences in personalities; such variables include self-reported stress, time spent exercising and contributions to charitable causes.
The survey’s primary measure of subjective well-being is an 11-point (from 0 to 10) life ladder, based on the question “Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” This question, also known as Cantril’s Self-Anchoring Ladder, is frequently used in well-being studies, including the recent World Happiness Report [13] and many studies cited therein. We plot the distribution of sample responses in the first panel of Figure 1. The mode is “7” with a quarter of the respondents. The next greatest concentration is “8” with about 20% of the responses. The sample mean is 6.8, significantly lower than for the Canadian ladder responses in the Gallup World Poll, as shown in figure 2.3 of the World Happiness Report.
It is possible to construct two other measures of well-being from the survey. One is life satisfaction, based on the four-point responses to the question “To what extent do you agree with each of the following statements” that include a statement “I am satisfied with my life”. The four points are “strongly agree”, “somewhat agree”, “somewhat disagree” and “strongly disagree”. The second panel of Figure 1 shows the distribution. The mode, covering more than 50% of the responses, is “somewhat agree”. Another potential measure is the response to the question “How happy are you at the beginning of 2011? Very happy, somewhat happy, somewhat unhappy, very unhappy.” The distribution of happiness is similar to that of life satisfaction: the third step “somewhat happy” has more than 50% of the sample. We will use these two measures of well-being for robustness tests.
There is also a question on the level of stress, specifically the response to the question “How would you rate your average daily stress levels? Very low, Low, Medium, High, Very high.” Its distribution is shown in the last panel of Figure 1. The response of “Medium” has the greatest share of responses at 40%.
We now move on to the two questions on social networks. The first question concerns real-life friends. The exact wording is “How big is your real-life social network of friends?” The permitted responses, unless the respondents refuse to answer, include “Less than 10 friends”, “Between 10 and 20 friends”, “Between 20 and 30 friends”, “Between 30 and 50 friends”, and “More than 50 friends”. The distribution of the network size is shown in the upper panel of Figure 2. A large majority of the sample, almost 80%, is in the first two categories (i.e., with fewer than 20 friends).
The immediately next question in the survey concerns online friends: “How big is your online social network?” The responses include “I don't have an online social network”, “Less than 100”, “Between 100–300”, “Between 300–500”, “Between 500–700” and -Greater than 700”. The distribution is shown on the lower panel of Figure 2. A large majority of the sample either has no online friends (about 25%) or have fewer than 100 of them (about 50%).
The two network questions have different numbers of steps, and both have some steps with sparse responses (see Figure 2). We correct for these problems by combining the top two categories of real-life network into one single category with 11% of the sample, and the top three categories of online network into one category with 9% of the sample. This way, we turn the two network sizes into a comparable scale of four steps. In the case of real-life network, the four categories are “less than 10”, “10–20”, “20–30” and “30 or more”, with 44%, 34% 11% and 11% of the sample, respectively. The size of online network falls into “0”, “1–100”, “100–300”, “300 or more”, with 23%, 50.8%, 17.6% and 8.6% of the sample, respectively.
Table 1 presents summary statistics of other variables. The average age is 45. Forty-five percent (45%) of the sample are married; 15% in common-law relation, 5% dating, 23% single; the remaining 12% are divorced, separated, widowed or are unknown. The income information is based on categorical responses of income intervals. We estimate the midpoint of each interval under the assumption that income follows a log-normal distribution. We then assign respondents in each interval the corresponding midpoint estimate. The categories for the income variables are “$20,000 and below”, “$20,001 to $35,000”, “$35,001 to $50,000”, “$50,001 to $75,000”, “$75,001 and $110,000” and “more than $110,000”. The estimated midpoints are $13,605, $27,073, $41,895, $60,345, $87,895 and $136,849 respectively. About 15% of the sample did not provide income information. We use a dummy variable to indicate such a status in the regression analysis. Among those that have valid income information, the sample mean is $51 thousand. The average time spent on moderate to high intensity exercising is 1.78 hours per week. Close to 60% of survey respondents indicated that they currently volunteer or give time or money to charitable causes.
A second dataset that we use is the European Social Survey (ESS), a biennial cross-sectional survey of residents aged 15 and over within private households that is “designed to chart and explain the interaction between Europe's changing institutions and the attitudes, beliefs and behaviour patterns of its diverse populations” (The European Social Survey project). We use the cumulative file for rounds 1–4 (2002, 2004, 2006, 2008) that has 34 participating countries. The ESS does not have information relating to online social networks. Instead, it has information on survey respondents’ frequency of socially meeting with friends, relatives or colleagues. Figure 3 plots the distribution of the frequency, in the categories of “Never”, “Less than once a month”, “Once a month”, “Several times a month”, “Once a week”, “Several times a week” and “Every day”.
The ESS has two alternative measures of SWB, happiness and life satisfaction. The two underlying questions are “Taking all things together, how happy would you say you are?” and “All things considered, how satisfied are you with your life as a whole nowadays?”. Both SWB measures are on a 11-point ascending scale from 0 to 10, with 0 indicating extremely unhappy/dissatisfied and 10 indicating extremely happy/satisfied. Figure 4 plots the distributions. Table 2 presents summary statistics of other variables.
4. Social Relationships and Mortality Risk: A Meta-analytic Review
Abstract
Background
The quality and quantity of individuals' social relationships has been linked not only to mental health but also to both morbidity and mortality.Objectives
This meta-analytic review was conducted to determine the extent to which social relationships influence risk for mortality, which aspects of social relationships are most highly predictive, and which factors may moderate the risk.Data Extraction
Data were extracted on several participant characteristics, including cause of mortality, initial health status, and pre-existing health conditions, as well as on study characteristics, including length of follow-up and type of assessment of social relationships.Results
Across 148 studies (308,849 participants), the random effects weighted average effect size was OR = 1.50 (95% CI 1.42 to 1.59), indicating a 50% increased likelihood of survival for participants with stronger social relationships. This finding remained consistent across age, sex, initial health status, cause of death, and follow-up period. Significant differences were found across the type of social measurement evaluated (p<0 .001="" 0.99="" 1.19="" 1.44="" 1.63="" 1.91="" 2.23="" 95="" alone="" and="" association="" binary="" ci="" complex="" for="" indicators="" integration="" living="" lowest="" measures="" of="" others="" p="" residential="" social="" status="" strongest="" the="" to="" versus="" was="" with="">Conclusions
The influence of social relationships on risk for mortality is comparable with well-established risk factors for mortality.Editors' Summary
Background
Humans are naturally social. Yet, the modern way of life in industrialized countries is greatly reducing the quantity and quality of social relationships. Many people in these countries no longer live in extended families or even near each other. Instead, they often live on the other side of the country or even across the world from their relatives. Many also delay getting married and having children. Likwise, more and more people of all ages in developed countries are living alone, and loneliness is becoming increasingly common. In the UK, according to a recent survey by the Mental Health Foundation, 10% of people often feel lonely, a third have a close friend or relative who they think is very lonely, and half think that people are getting lonelier in general. Similarly, across the Atlantic, over the past two decades there has been a three-fold increase in the number of Americans who say they have no close confidants. There is reason to believe that people are becoming more socially isolated.Why Was This Study Done?
Some experts think that social isolation is bad for human health. They point to a 1988 review of five prospective studies (investigations in which the characteristics of a population are determined and then the population is followed to see whether any of these characteristics are associated with specific outcomes) that showed that people with fewer social relationships die earlier on average than those with more social relationships. But, even though many prospective studies of mortality (death) have included measures of social relationships since that first review, the idea that a lack of social relationships is a risk factor for death is still not widely recognized by health organizations and the public. In this study, therefore, the researchers undertake a systematic review and meta-analysis of the relevant literature to determine the extent to which social relationships influence mortality risk and which aspects of social relationships are most predictive of mortality. A systematic review uses predefined criteria to identify all the research on a given topic; a meta-analysis uses statistical methods to combine the results of several studies.What Did the Researchers Do and Find?
The researchers identified 148 prospective studies that provided data on individuals' mortality as a function of social relationships and extracted an “effect size” from each study. An effect size quantifies the size of a difference between two groups—here, the difference in the likelihood of death between groups that differ in terms of their social relationships. The researchers then used a statistical method called “random effects modeling” to calculate the average effect size of the studies expressed as an odds ratio (OR)—the ratio of the chances of an event happening in one group to the chances of the same event happening in the second group. They report that the average OR was 1.5. That is, people with stronger social relationships had a 50% increased likelihood of survival than those with weaker social relationships. Put another way, an OR of 1.5 means that by the time half of a hypothetical sample of 100 people has died, there will be five more people alive with stronger social relationships than people with weaker social relationships. Importantly, the researchers also report that social relationships were more predictive of the risk of death in studies that considered complex measurements of social integration than in studies that considered simple evaluations such as marital status.What Do These Findings Mean?
These findings indicate that the influence of social relationships on the risk of death are comparable with well-established risk factors for mortality such as smoking and alcohol consumption and exceed the influence of other risk factors such as physical inactivity and obesity. Furthermore, the overall effect of social relationships on mortality reported in this meta-analysis might be an underestimate, because many of the studies used simple single-item measures of social isolation rather than a complex measurement. Although further research is needed to determine exactly how social relationships can be used to reduce mortality risk, physicians, health professionals, educators, and the media should now acknowledge that social relationships influence the health outcomes of adults and should take social relationships as seriously as other risk factors that affect mortality, the researchers conclude.5. Practicing Gratitude Can Increase Happiness by 25%
Psychological research finds that people’s happiness levels are remarkably stable over the long-term. Whether you win the lottery or are paralysed from the neck down, after about three to six months you’ll have returned to your usual level of happiness. While these findings are deeply counter-intuitive, they also raise a serious problem for those wanting to increase levels of happiness permanently.
A possible answer comes from recent research in the psychology of gratitude. Yes, you read that correctly – being thankful might be the key to raising your happiness ‘set-point’. And there is some good experimental evidence to back up this theory.
Counting blessings versus burdens
In his new book ‘thanks!‘, Dr. Robert A. Emmons describes research he carried out with three experimental groups over 10 weeks (Emmons & McCullough, 2003):experience and more articles.
- The first group were asked to write down five things they were grateful for that had happened in the last week for each of the 10 weeks of the study. This was called the gratitude condition.
- The second group were asked to write down five daily hassles from the previous week. This was the hassles condition.
- The third group simply listed five events that had occurred in the last week, but not told to focus on positive or negative aspects. This was the events or control condition.
- Sunset through the clouds.
- The chance to be alive.
- The generosity of friends.
- Taxes.
- Hard to find parking.
- Burned my macaroni and cheese.
Happiness up 25%
People who were in the gratitude condition felt fully 25% happier – they were more optimistic about the future, they felt better about their lives and they even did almost 1.5 hours more exercise a week than those in the hassles or events condition.Just the effect of positive comparisons, or really gratitude?
Yet, despite these reasons why gratitude might not increase happiness, it seems that it does. But does the benefit from the gratitude condition simply result from thinking about how we are better off than others?In a second study, very similar to the first described above, Emmons and McCullough changed one of the control conditions. Instead of asking people to write down any events from the week, people were asked to list ways in which they were better off than others. The idea was that in this condition people are making positive comparisons but are not necessarily thinking gratefully (although it can’t be ruled out!).
Again, though, the results showed that those in the gratitude condition were significantly happier than those making positive comparisons between themselves and others. Unsurprisingly those practising being grateful were also happier than those focussing on daily hassles.
Gratitude can help those with chronic health problems
A good criticism of the first two studies was that they were carried out in undergraduate students. It’s all very well increasing the happiness of young, healthy college students, but what about people with serious, chronic health problems?In a third study Emmons and McCullough recruited adults who had neuromuscular disorders, often as a delayed result of surviving infection by the polio virus. While not life-threatening the condition can be seriously debilitating, causing joint and muscle pain as well as muscle atrophy. People with this condition have a good reason to be dissatisfied with the hand life has dealt them.
In this study a gratitude condition was compared to a control condition in which participants wrote about their daily experience. After the 21 day study, participants in the gratitude condition were found to be more satisfied with their lives overall, more optimistic about the upcoming week and crucially, were sleeping better. Good sleep is important as it has been found to be a great indicator of overall well-being. People who sleep well are generally healthier and happier than those whose sleep is poor.
Practising gratitude
Even if gratefulness has benefits in the short-term, it still raises more long-term questions. What are the major obstacles to living a grateful life? Can gratefulness really increase happiness over a lifetime? Finally, how exactly can gratefulness be increased? It’s this last question that I’ll be addressing in the next post with Dr Emmons’ top ten methods for practising gratitude.6. PROVIDING SOCIAL SUPPORT MAY BE MORE BENEFICIAL THAN RECEIVING IT:
Results From a Prospective Study of Mortality
AbstractAs demographic shifts have produced a relatively more aged population, factors that influence longevity have taken on increased prominence. The documented health benefits of social support may offer a promising avenue for reducing mortality among older adults. Indeed, there is a robust association between social contact and health and well-being (House, Landis, & Umberson, 1988). However, it is not clear that receiving support accounts for these benefits (House et al., 1988). Tests of the social-support hypothesis—that receiving support improves health and well-being—have provided somewhat inconsistent results (Kahn, 1994), demonstrating in some instances that receiving support is harmful (e.g., S.L. Brown & Vinokur, in press; Hays, Saunders, Flint, Kaplan, & Blazer, 1997; Seeman, Bruce, & McAvay, 1996). In fact, a meta-analysis of the link between social support and health outcomes produced negligible findings, leading the study’s authors to conclude that the "small amounts of shared variance [between receiving support and health outcomes] may not be considered significant nor generalizable” (Smith, Fernengel, Holcroft, Gerald, & Marien, 1994, p. 352).
This study examines the relative contributions of giving versus receiving support to mortality in a sample of older married adults. Baseline indicators of giving and receiving support were used to predict mortality status over a 5-year period in the Changing Lives of Older Couples sample. Results from logistic regression analyses indicated that mortality was significantly reduced for individuals who reported providing instrumental support to friends, relatives, and neighbors, and individuals who reported providing emotional support to their spouse. Receiving support had no effect on mortality once giving support was taken into consideration. This pattern of findings was obtained after controlling for demographic, personality, health, mental health, and marital-relationship variables. These results have implications for understanding how social contact influences health and longevity.
Conceptually, it is not clear that receiving social support will always be beneficial. For example, depending on other people for support can cause guilt and anxiety (Lu & Argyle, 1992). And feeling like a burden to others who presumably provide support is associated with increased suicidal tendencies, even after controlling for depression (R.M. Brown, Dahlen, Mills, Rick, & Biblarz, 1999; de Catanzaro, 1986). The correlation of social support with dependence may help to explain why studies have failed to consistently confirm the social-support hypothesis. (source click here)
Furthermore, the benefits of social contact may extend beyond received support to include other aspects of the interpersonal relationship that may protect health and increase longevity—for example, giving support to others. However, with few exceptions (e.g., Liang, Krause, & Bennett, 2001), social-support studies rarely assess whether there are benefits from providing support to others. Some measures of social support do seem to tap giving—perhaps inadvertently—yet the benefits are often attributed to receiving support or sometimes attributed to reciprocated support. For example, a nationwide survey of older peoples' support networks measured social support by a combination of what was received and what was provided to others (Antonucci, 1985). Implicit in this assessment is the recognition that receiving social support is likely to be correlated with other aspects of close relationships, including the extent to which individuals give to one another. Thus, some of the benefits of social contact, traditionally attributed to receiving support, or to reciprocated support (e.g., Antonucci, Fuhrer, & Jackson, 1991), may instead be due to the benefits of giving support.
THE BENEFITS OF PROVIDING SUPPORT TO OTHERS
There are both theoretical and empirical reasons to hypothesize that giving support may promote longevity. For example, kin-selection theory (Hamilton, 1964a, 1964b) and reciprocal-altruism theory (Trivers, 1971) suggest that human reproductive success was contingent upon the ability to give resources to relationship partners. Social bonds (S.L. Brown, 1999) and emotional commitment (Nesse, 2001) have been theorized to promote high-cost giving. The resulting contribution made to relationship partners is theorized to trigger a desire for self-preservation on the part of the giver, enabling prolonged investment in kin (de Catanzaro, 1986) and reciprocal altruists.
Although few studies have explicitly examined whether helping others increases longevity, sociologists note the ubiquity of giving to others (Rossi, 2001), and studies show that individuals derive benefits from helping others, such as reduced distress (Cialdini, Darby, & Vincent, 1973; Midlarsky, 1991) and improved health (Schwartz & Sendor, 2000). Moreover, volunteering has beneficial effects for volunteers, including improved physical and mental health (Omoto & Synder, 1995; Wilson & Musick, 1999). Even perceptions that are likely to be associated with giving, such as a sense of meaning, purpose, belonging, and mattering, have been shown to increase happiness and decrease depression (e.g., Taylor & Turner, 2000; see Batson, 1998, for a review).
THE PRESENT STUDY
Using data from the Changing Lives of Older Couples (CLOC) sample, we addressed two questions: (a) Do the benefits of providing social support account for some or all of the benefits of social contact that are traditionally interpreted as due to support received from others? (b) Does receiving support influence mortality once giving support and dependence are controlled?
Traditionally, social support has been defined in numerous ways, leading some authors to conclude that measurement issues are a source of contradictory findings (e.g., Smerglia, Miller, & Kort-Butler, 1999). For the purpose of the present study, we focused our analyses on items for which our measures of giving and receiving tapped similar domains of support. Similar domains of support were measured for the exchange of emotional support between spouses and the exchange of instrumental support with individuals other than one’s spouse. House (1981) suggested that these two domains of support—emotional and instrumental— represent two of the four functions of interpersonal transactions.
To isolate the unique effects of giving and receiving social support on mortality, it was important to control for factors that may influence any of these variables, including age, gender, perceived health, health behaviors, mental health, socioeconomic status, and some individual difference variables (personality traits). Controlling for these variables helped to increase our confidence that any beneficial effect of giving we observed was not due to enhanced mental or physical robustness of the giver. We also examined variables associated with relationship phenomena that could influence giving support, receiving support, and dependence; these variables included perceived equity (the perception that one receives the same amount as one provides to the relationship partner) and relationship satisfaction. Responses at baseline were used to predict mortality status over the ensuing 5-year period of the study.
METHOD
Sample
The CLOC study is a prospective study of a two-stage area probability sample of 1,532 married individuals from the Detroit Standard Metropolitan Statistical Area. The husband in each household was 65 years of age or older (see Carr et al., 2000, for a complete report). Of those individuals who were selected for participation in the CLOC study, 65% agreed to participate, a response rate consistent with response rates in other studies in the Detroit area (Carr et al., 2000). More than one half of the sample (n = 846) consisted of married couples for whom mortality data on both members was available. These 423 married couples were the respondents in the present study. 1 Baseline measures were administered in face-to-face interviews, conducted over an 11-month period in 1987 and 1988. Of the subsample of 846 respondents, 134 died over the 5-year course of the study.
Mortality Data
Mortality was monitored over a 5-year period by checking daily obituaries in three Detroit-area newspapers and monthly death-record tapes provided by the State of Michigan. Mortality status was indicated with a dichotomous variable (1 = deceased, 0 = alive).
Baseline Measures
Instrumental support
Giving instrumental support to others, GISO, was measured by four survey questions that asked respondents whether they had given instrumental support to friends, neighbors, and relatives other than their spouse in the past 12 months. Respondents indicated (yes/no) whether they helped with (a) transportation, errands, shopping; (b) housework, (c) child care, and (d) other tasks. Respondents were instructed to say “yes” to any of these questions only if they did not live in the same household with the recipient of support and they did not receive monetary compensation. Responses were coded so that a “0” indicated a “no” response to all four items, and a “1” indicated a “yes” response to at least one item.
Receiving instrumental support from others, RISO, was assessed by a single item: “If you and your husband [wife] needed extra help with general housework or home maintenance, how much could you count on friends or family members to help you?” Responses were coded on a 4-point scale. [1]
Emotional support
Giving and receiving emotional support was assessed with items from the Dyadic Adjustment Scale (Spanier, 1976). Giving emotional support to a spouse, GESS, was assessed using two items that asked participants whether they made their spouse feel loved and cared for and whether they were willing to listen if their spouse needed to talk (a = .51). Rankin-Esquer, Deeter, and Taylor (2000) reviewed evidence to suggest that the benefits of receiving emotional support from a spouse come from both feeling emotionally supported by a spouse and feeling free to have an open discussion with one’s spouse. The two-item measure of receiving emotional support from a spouse, RESS, (a = .66) was identical with the exception that participants were asked whether their spouse made them feel loved and cared for, and whether their spouse was willing to listen if they needed to talk. Responses were coded on a 5-point scale. [2]
Control variables
To control for the possibility that any beneficial effects of giving support are due to a type of mental or physical robustness that underlies both giving and mortality risk, we measured a variety of demographic, health, and individual difference variables. (See Appendix A for a description of the health, mental health, and personality variables used.) Both age and gender (1 = male, 2 = female) were controlled for in each analysis to take into account the possibilities that (a) older people give less and are more likely to die than younger people and (b) females give more and are less likely to die than males.
To isolate the unique effects of giving and receiving support, above and beyond other known relationship influences on health, we included measures of social contact and dependence. Social contact was assessed with the mean of the following three questions: “In a typical week, about how many times do you talk on the phone with friends, neighbors, or relatives?” “How often do you get together with friends, neighbors, or relatives and do things like go out together or visit in each other’s homes?” and “How often do you go out socially, by yourself, or with people other than your husband [wife]?” Scores were standardized so that higher values indicated greater social contact (a = .51).
Dependence on the spouse was coded on a 4-point scale and was measured with three items asking participants whether losing their spouse would make them feel lost, be terrifying, or be the worst thing that could happen to them (a = .82).
Additional relationship variables
We measured additional aspects of the marital relationship in order to examine alternative explanations for any effects of giving and receiving emotional support. Specifically, we used items from the Dyadic Adjustment Scale (Spanier, 1976) to assess equity (the absolute value of the difference between an individuals' ratings of perceived emotional support received from the partner and perceived emotional support provided to the partner; higher values indicated greater discrepancy) and marital satisfaction (one item).
Additional measures of receiving and giving support
To consider the possibility that any observed benefits of giving or receiving support were an artifact of the chosen measures, we included all of the remaining support measures from the CLOC data set (Appendix B).
RESULTS
We examined our hypotheses using the 846 persons for whom mortality data were available. Because this sample included the responses of both members of a couple, we computed the intraclass correlation (ICC) for the couple-level effect on mortality. We first created a variable that grouped individual participants by couple (n = 423). We next constructed a two-level hierarchical model (Level 1 estimated variation in mortality at the individual-participant level, Level 2 estimated variation at the couple level) using RIGLS (restricted iterative generalized least squares) estimation for binomial models (MLwiN ver. 1.1, Multilevel Models Project, Institute of Education, London, 2000). A significant ICC could be interpreted as indicating that the death of one partner was significantly related to an increase or decrease in the probability of the other partner dying (within the study period). Results of this procedure indicated that there was no couple-level effect on mortality (ICC = .00, n.s.). Thus, for all analyses, we treated each member of a couple as an independent source of data.
Giving Support, Receiving Support, and Social Contact
Table 1 presents a correlation matrix of the focal social-support measures. Receiving and giving were significantly and strongly correlated for measures of emotional support exchanged between spouses (r = .58, p < .001), and weakly correlated for measures of instrumental support exchanged with others (r = .09, p < .01).
To examine whether giving instrumental support reduced risk of mortality, we ran a hierarchical logistic regression procedure. Results of this analysis are displayed in Fig. 1, and the odds ratios are presented in Table 2. Step 1 of this analysis regressed mortality status on social contact, age, and gender. The results were consistent with previous research in indicating that social contact reduced the risk of mortality (b = -0.21, p < .05). To examine whether giving versus receiving support accounted for this effect, we entered GISO and RISO simultaneously in the second step. Results at this step indicated that mortality risk was decreased by GISO (b = -0.85, p < .001) but increased by RISO (b = 0.17, p < .10). Social contact was no longer significant at this step (b = -0.13, n.s.).
Because individuals in poor health may have difficulty providing others with instrumental support, functional health status, satisfaction with health, health behaviors, and mental health variables were added to the model in order to control for the alternative possibility that individuals who give support to others live longer because they are more mentally and physically robust than those who do not give support.
Results at this step indicated that after controlling for these measures of health, the effect of GISO was reduced, but GISO was still significantly related to mortality (b = -0.56, p < .01). In fact, GISO exerted a beneficial effect on mortality even after controlling for interviewer ratings of health, income and education level, self-reports of feeling vulnerable to stress, dispositional influences on mortality, and personality influences on mortality. After all control variables were held constant, GISO significantly decreased mortality risk (b = -0.54, p < .05), and RISO marginally increased mortality risk (b = 0.23, p < .10).
These results support the hypothesis that giving support accounts for some of the benefits of social contact. However, our findings are based on the use of different measures to operationalize giving and receiving support. That is, the GISO variable measured support that was actually provided to other people (i.e., enacted support), whereas the RISO variable assessed whether others could be depended upon to provide support (i.e., available support).4 Furthermore, it is not clear whether the adverse effect of RISO was due to received support or to the covariation of received support with dependence. In order to control for the difference between the giving and receiving measures, as well as the potentially adverse effect of dependence, we examined the exchange of emotional support between spouses. This domain of support offered virtually identical giving and receiving measures, and included measures of dependence.
Analyses With Identical Measures of Giving and Receiving Support
To clarify the role of receiving support on mortality, we ran a hierarchical logistic regression procedure in which RESS was entered in Step 1, along with age and gender. As can be seen in Figure 2, there was no significant effect of RESS on the risk of mortality (b = -0.17, n.s.). However, after controlling for the effect of dependence in Step 2, the effect of RESS became a significant predictor of reduced mortality risk (b = -0.23, p < .05). Thus, the results of Step 2 replicated the typical beneficial effect of receiving support found in the literature—but only after the adverse effect of dependence was held constant.
To compare the relative benefits of receiving versus giving support using identical measures, we entered GESS on the third step of this analysis. As shown in Figure 2, the unique effect of GESS accounted for a significant decrease in mortality risk (b = -0.36, p < .05), and rendered the effect of RESS nonsignificant (b = -0.05, n.s.). In order to examine whether GESS remained beneficial after controlling for GISO and the cumulative effect of all of the control variables, we entered GESS into the hierarchical regression model presented in Table 2 (Step 5). Results of this analysis demonstrated that both GESS (b = -0.51, p < .01) and GISO (b = -0.50, p < .05) made a unique, significant contribution to reducing mortality risk, above and beyond that of the control variables. Thus giving to one’s spouse (GESS) and giving to friends, relatives, and neighbors (GISO) both appear to exert an independent influence on the reduction in risk of mortality.
Finally, we examined two additional relationship factors that may be related to giving support--equity and marital satisfaction. We first added marital satisfaction to the overall model (shown in Table 2 and Fig. 1); it was not a significant predictor of mortality (b = -0.15, n.s.), nor did it affect the strength of any of the other predictors. We ran a similar model for equity, without GESS and RESS. Equity did not predict mortality (b = 0.20, n.s.).
Additional Measures of Receiving and Giving
Because the CLOC data included additional measures of giving and receiving, it was possible to determine whether our pattern of results was simply an artifact of the measures chosen. To examine this possibility, we correlated mortality status with each of the giving and receiving measures available in the CLOC data set. In addition, the composites for giving support were broken down into single items and correlated independently with mortality status. As shown in Table 3, only 1 of the 10 different receiving measures significantly reduced mortality risk5; 1 receiving measure significantly increased mortality risk. In contrast, all 4 of the different giving measures significantly reduced mortality risk. When the composites for giving support were broken down, 4 of the 6 items were significantly correlated with decreased mortality risk, including the only item that assessed available, rather than enacted, support. Taken together, these findings strongly suggest that giving support, rather than receiving support, accounts for the benefits of social contact, across different domains of support, different targets of support, and different structural features of support.
DISCUSSION
In this study, older adults who reported giving support to others had a reduced risk of mortality. The provision of support was correlated with reduced mortality in all analyses, whether giving support was operationalized as instrumental support provided to neighbors, friends, and relatives or as emotional support provided to a spouse. It is important to note that our analyses controlled for a wide range of demographic, personality, and health variables that might have accounted for these findings. Thus, these results add to a small but growing body of research that documents the health benefits of providing support to others (McClellan, Stanwyck, & Anson, 1993; Midlarsky, 1991; Schwartz & Sendor, 2000).
We also found that the relationship between receiving social support and mortality changed as a function of whether dependence and giving support were taken into consideration. Receiving emotional support (RESS) appeared to reduce the risk of mortality when dependence but not giving emotional support was controlled. Receiving instrumental support from others appeared to increase the risk of mortality when giving support, but not dependence, was controlled. Taken together, these findings may help to explain why tests of the social-support hypothesis have produced contradictory results. If the benefits of social contact are mostly associated with giving, then measures that assess receiving alone may be imprecise, producing equivocal results.
Although we have identified no single mediator of the link between giving support and mortality—one that could be informative about the process underlying the beneficial effects of giving support—many social psychological studies show that helping others increases positive emotion (e.g., Cialdini & Kenrick, 1976). Positive emotions, in turn, have been demonstrated to speed the cardiovascular recovery from the aftereffects of negative emotion (Fredrickson, Mancuso, Branigan, & Tugade, 2000). Thus, helping may promote health through its association with factors, such as positive emotion, that reduce the deleterious effects of negative emotion. Research is currently under way to examine this possibility.
More broadly, our results are consistent with the possibility that the benefits of social contact are shaped, in part, by the evolutionary advantages of helping others. The effects of giving support accounted for the benefits of both social contact and receiving support. In an evolutionary context, this result can be interpreted as indicating that older adults may still be able to increase their inclusive fitness (the reproductive success of individuals who share their genes) by staying alive and prolonging the amount of time they can contribute to family members (de Catanzaro, 1986). Of course this possibility relies on the assumption that a motivation for self-preservation can influence mortality. In fact, there is evidence to suggest that individuals with a “fighting spirit” survive longer with cancer than individuals who feel helpless or less optimistic about their chance of survival (Greer, Morris, & Pettingale, 1994).
Limitations and Directions for Future Research
Although the prospective, longitudinal design of this study is very strong, given the outcome of interest, alternative explanations for these findings remain viable. It may be, for example, that giving support is a better measure of health than receiving support, or that individuals who have the resources and motivation to give are also more robust than those who do not, or that an abundance of resources promotes longevity and makes it easier to give. We attempted to control for these alternative possibilities by demonstrating that the beneficial effects of giving support are above and beyond the effects of age, functional health, satisfaction with health, health behaviors, mental health, interviewer ratings of health, socioeconomic status, and vulnerability to stress. Moreover, two distinct types of giving–GESS and GISO–contributed simultaneously to longevity. This means that a third variable correlated with one measure of giving—such as robustness of one’s health—would have been held constant in a model that simultaneously tested the effect of the other giving measure. Thus, it is unlikely that the same alternative explanation can account for both effects of giving support. Of course, given the correlational nature of the study design, the regression methods used to disentangle these alternatives do not give the confidence that would be achieved by an experimental design. Nonetheless, longitudinal prospective studies like the one described here are important precursors to eventual long-term (and large-scale) experimental interventions that promote giving support.
It would be premature, on the basis of a single study, to conclude that giving support accounts for the traditional effects of receiving social support found in the literature (to our knowledge, no other studies have advanced this hypothesis). Nevertheless, the results of the present study should be considered a strong argument for the inclusion of measures of giving support in future studies of social support. Perhaps more important, our results corroborate the suggestion by House and his colleagues (1988) that researchers should be cautious of assuming that the benefits of social contact reside in the supportive quality of the relationship. Thus, whether or not mortality risk is a function of giving support, our results highlight the continued need for further research to seriously examine the fundamental assumption guiding the study of social support.
Conclusion
Giving support may be an important component of interpersonal relationships that has considerable value to health and well-being. It may not be a coincidence that mortality and morbidity studies inadvertently assess giving or manipulate giving (e.g., taking care of a plant; Rodin & Langer, 1977) to operationalize variables of interest such as receiving social support and locus of control. If giving, rather than receiving, promotes longevity, then interventions that are currently designed to help people feel supported may need to be redesigned so that the emphasis is on what people do to help others. The possibility that giving support accounts for some of the benefits of social contact is a new question that awaits future research. (source click here)
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