Affect Specificity as Indicators of National Well-Being: Representative Sample of Croatia

  • Zvjezdana Prizmić-Larsen
  • Ljiljana Kaliterna Lipovčan
  • Tihana Brkljačić
Chapter

Abstract

In our survey-based study, we gathered evaluative and experiential measures of well-being and examined their correlates with different life domains variables used as predictors for each well-being measure. Subjects were a representative sample of Croatian citizens (N  =  1,129). Subjects reported life satisfaction and rated how often they felt happy, satisfied, sad, angry, depressive, and stressed over the past month. Predictors variables included measures representing various domains such as physical (health), social (seeing friends, family support, receiving help, trust, fairness), psychological (learning, respect, recognition, spirituality), and job-related variables (job satisfaction, commuting). Hierarchical regression analyses were used with age, gender, and income as covariates. Distinctive predictors of positive emotions were learning and seeing friends, while predictors of negative emotions were trust, fairness, and recognition.

Keywords

Life Satisfaction Negative Emotion Positive Emotion Family Support Social Trust 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Growing evidence suggests that besides the economic indicators, subjective well-being should also be taken into account when measuring national welfare (Deaton, 2008; Diener, Kesebir, & Lucas, 2008; Diener & Seligman, 2004; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). Evaluating national well-being can help policy makers by giving them information about domains where quality of life needs to be improved and about possible side effects of implemented measures (Diener et al., 2008; Lyubomirsky, King, & Diener, 2005). As such, they can complement existing economic indicators of national welfare in determining the developmental progress of the nation.

Measuring population well-being and examining the impact of individual well-being on the wealth of society are increasing in countries around the world and in some instances become a standard follow-up procedure (Bonini, 2008; Deaton, 2008; Selim, 2008). For example, Gallup started gathering, systematically, data on many aspects of respondents’ well-being and their behaviors from 132 countries worldwide, where the samples are nationally representative (Deaton, 2008). This makes opportunity for cross-country comparisons. Many national and international surveys are using measures that allow the comparison of well-being across different nations, subpopulations, regions, and other groups of interest (Bonini, 2008; Kaliterna Lipovčan & Prizmić-Larsen, 2006a, 2006b; 2007; Selim, 2008).

Researchers distinguish between cognitive and affective components of well-being (Diener, 2006; Kuppens, Realo, & Diener, 2008). The cognitive component refers to people’s subjective evaluation of their life circumstances, while the affective component refers to the balance of positive and negative affective states experienced over time. Researches show that positive and negative affect should be measured separately since they are relatively independent constructs displaying different relationships with predictor variables (Huppert & Whittington, 2003; Lucas, Diener, & Suh, 1996). For example, Huppert and Whittington (2003) found that unemployment was strongly related to lack of positive affect and weakly to negative affect, while social support was strongly related to negative affect and weakly to positive affect.

Regarding the ways of measuring well-being, Kahneman and Krueger (2006) support the distinction between “experienced utility,” which is the way people feel about an experience in real time, and “remembered utility,” which refers to the way people remember their experience after it is over. They pointed out the differences between the measures which elicit a global evaluation of one’s life and the measures of experienced affect or happiness reported in real time (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2006). Depending on the purpose of the research, well-being indicators vary from evaluative assessments of global life satisfaction and specific life domains satisfaction to momentary or time-related assessments of subjective experience (Diener, 2006; Kahneman & Krueger, 2006; Kahneman et al., 2006; Kuppens et al., 2008).

The predictors of well-being have been researched widely, as well as the factors associated with it (Diener and Seligman, 2004; Oishi, Diener, Lucas, & Suh, 1999). The links between well-being measures and factors such as income, health, marital status, age, sex, job moral, and education have been demonstrated (for review see Diener and Seligman, 2004; Diener, Suh, Lucas, & Smith, 1999; Dolan, Peasgood, & White, 2008). In recent years, the research questions about how human behavior shapes and influences positive growth in society are also of interest to other disciplines. For instance, behavioral economics, which incorporates well-being indicators with existing economic ones, connect two discipline, psychology and economics, to investigate psychological aspects of different economic activity (Hobcraft, 2006; Kahneman & Krueger, 2006).

Besides the widely used well-being measures, such as happiness and life satisfaction, which represent hedonic well-being, the importance of eudaimonic well-being, which refers to positive psychological and social functioning in life and human potential, is also emphasized (Diener et al., 1999; Keyes, Shmotkin, & Ryff, 2002). Psychological well-being is tapping into areas of personal growth and development, feelings of respect and reward, while social well-being is concerned with quality of a person’s relationship with other people, the community, and feeling of social trust (Deci & Ryan, 2008; Keyes et al., 2002; Ryan & Deci, 2001).

The main goal of our study was to examine different life domains as predictors of evaluative and experiential measures of well-being on the nationally representative sample. The evaluative assessments of life satisfaction and assessment of six emotions (two positive and four negative) experienced over the past month were used as well-being measures. We examined their correlates with different variables covering the physical, social, and psychological domains.

Method

Subjects

Subjects were a representative sample of 1,129 Croatian citizens recruited as a part of a public opinion research project (November 2007). Median age was 49 years (range 18–95 years). Women made up 56% of the sample and men 44%.

In terms of household monthly income, 17% reported to have less than 137 Euros1 per family member, 33% had between 137 and 273 Euros, 34% had between 273 and 546 Euros, and 16% reported to have more than 546 Euros per family member.

Procedure

The data were obtained from the national survey on public opinion conducted in November 2007 in Croatia. The survey was carried out by in-person interviews in the respondents’ home. Interviews were done by trained persons who attended training sessions on the use of the questionnaire and procedures. The Troldahl–Carter method of selecting adult respondents within households was used to achieve a balance of males and females, younger and older adults in the sample. The respondents were told that their responses were anonymous.

Measures

Well-Being Variables

As evaluative assessment measures of well-being, we used measures of life satisfaction. As experiential measures of well-being, we measured specific emotions experienced over the past month.

Life satisfaction. As a measure of global life satisfaction, one-item life satisfaction measure was used. Subjects rated how much they were satisfied with their life as a whole using an 11-point scale, where 0 was “totally unsatisfied” and 10 was “totally satisfied.”

Specific emotions. The ratings of specific emotions were obtained by asking subjects about their emotions over the past month. They reported how often they experienced two positive (i.e., happy and satisfied) and four negative emotions (i.e., sad, angry, depressed, and stressed) over the past month using the seven-point scale where 1 was “almost never” and 7 was “almost always.”

Life Domains Variables

As predictors of well-being, various measures representing different domains such as physical domain (i.e., health), social domain (i.e., social and family support, trust and fairness) and psychological domain (i.e., learning, respect, recognition and spirituality) were included.

Physical domain. Perceived health was measured with one item “In general, how would you describe your health?” on the scale of 1 as “very poor”and 5 as “excellent.”

Social domain. Social and family support questions covered aspects such as the frequency of seeing friends, receiving support from the community, and family support. Question concerning seeing friends asked about the frequency of meeting friends or work colleagues socially and rated it on a seven-point scale from 1 as “never” to 7 as “everyday.” Receiving support was assessed by asking subjects if they thought that people in a community help one another and rated it on a seven-point scale, from 1 as “never” to 7 as “always.” Family support question asked subjects if they can ask their family for help and rated it on a five-point scale, from 1 as “never” to 5 as “always.” Higher results on each question reflected better social and family support.

Trust. Perception of trust in society was examined by the question “Would you say that most people could be trusted?” and the subject had to rate it on an 11-point scale, from 0 as “cannot be trusted” to 10 as “could be completely trusted.”

Fairness. Perception of fairness in society was examined by the question “Do you think that most people would try to take advantage of you?” and the subject rated it on an 11-point scale, from 0 as “most people would take advantage” to 10 as “most people would be fair to me.”

Psychological domain. Questions in this domain covered aspects of personal growth such as learning, respect, recognition and spirituality. Learning was covered by asking subjects to report if they get a chance to learn new things. Respect was examined by asking subjects if people respect them, while recognition by asking them if they feel that they get the recognition they deserve for what they do. All three questions were rated on a seven-point scale, from 1 as “never” to 7 as “almost always.” Spirituality was assessed by questioning how much religion was important in their life, which was rated on a five-point scale, from 1 as “completely unimportant” to 5 as “extremely important.”

Results

Descriptive statistics for the well-being and life domains variables are presented in Table 3.1.
Table 3.1

Descriptive statistics for the sample

  

Theoretical range

Mean

SD

Well-being

Life satisfaction

0–10

6.8

2.04

Specific emotions

Happy

1–7

4.9

1.19

Satisfied

1–7

4.9

1.21

Sad

1–7

3.4

1.34

Angry

1–7

3.5

1.25

Depressed

1–7

2.5

1.43

Stressed

1–7

3.2

1.70

Life domains variables

Physical domain

Health

1–5

3.0

1.10

Social domain

Seeing friends

1–7

5.4

1.46

Receiving help

1–7

4.4

1.14

Family support

1–5

4.4

0.94

Trust

0–10

5.2

1.98

Fairness

0–10

5.0

2.25

Psychological domain

Learning

1–7

4.4

1.44

Respect

1–7

5.2

1.06

Recognition

1–7

4.4

1.38

Spirituality

1–5

3.9

1.08

While evaluating their life from 0 as the “most unsatisfied” to 10 as “totally satisfied,” 61% of Croatian citizens reported 7 and higher satisfaction. On the low spectrum of the scale, 8% of Croatian citizens rated satisfaction with their life from 0 to 4, and the rest of Croatians, i.e., 31%, were between 5 and 6 on life satisfaction scores.

Among the various emotions experienced over the last month, the average person experienced the “most often” feelings of being happy and satisfied and the “least often” feeling of being depressed.

Well-Being and Predictors – the Regression Analyses

The correlations between well-being and predictors (socio-demographic and life domains variables) are shown in Table 3.2 and the inter-correlations between predictors’ variables in Appendix. Because of the very large sample size, most of the correlations are significantly different from zero.
Table 3.2

Zero-order correlations between well-being variables and predictors

 

Life satisfaction

Happy

Satisfied

Sad

Angry

Depressed

Stressed

Age

−.20**

−.26**

−.16**

.09**

−.21**

.05

−.21**

Gender

−.03

−.02

−.03

.22**

.08*

.12**

.14**

Income

.29**

.18**

.23**

−.15**

−.13**

−.08*

.02

Health

.32**

.35**

.31**

−.24**

.02

−.24**

−.01

Seeing friends

.14**

.25**

.20**

−.07*

−.03

−.10**

.01

Receiving help

.10**

.14**

.17**

−.08*

−.03

−.14**

−.08*

Family support

.23**

.22**

.23**

−.10**

−.07*

−.15**

−.04

Trust

.28**

.16**

.22**

−.20**

−.16**

−.21**

−.16**

Fairness

.22**

.15**

.21**

−.19**

−.16**

−.21**

−.15**

Learning

.23**

.30**

.26**

−.15**

−.01

−.13**

.04

Respect

.13**

.13**

.22**

−.14**

−.08*

−.22**

−.14**

Recognition

.17**

.12**

.20**

−.13**

−.13**

−.20**

−.18**

Spirituality

.04

−.02

.03

.01

.00

−.02

−.06*

*p  <  .05; **p  <  .01

The strongest correlation with well-being among the socio-demographic variables showed income, which was positively associated with life satisfaction and positive emotions and negatively associated with negative emotions except with feeling of being stressed.

Among the life domains predictors, health showed the highest correlations with well-being variables followed by variables of trust, fairness and learning. The lowest correlations were found between spirituality and well-being variables.

To explore the associations more systematically, hierarchical regression analyses were conducted separately on each of the well-being variables (life satisfaction, six emotions). Also, age, gender and family income were entered at the first step as covariates. Results are shown in Table 3.3.
Table 3.3

Summary of hierarchical regression analyses (beta weights) for life domains variables predicting life satisfaction and specific emotions

 

Life satisfaction

Emotions

Happy

Satisfied

Sad

Angry

Depressive

Stressed

Step 1

Age

−.14**

−.24**

−.11**

.06*

−.24**

.04

−.21**

Gendera

−.01

−.02

−.02

.22**

.07*

.12**

.13**

Income

.26**

.13**

.20**

−.12**

−.17**

−.04*

−.01

R

.32**

.29**

.26**

.27**

.28**

.15**

.25**

Step 2

Age

−.01

−.05

.05

−.05

−.29**

−.09*

−.25**

Gender

−.02

−.02

−.03

.23**

.07*

.13**

.14**

Income

.18**

.04

.12**

−.05

−.13**

.01

.03

Health

.20**

.21**

.20**

−.21**

−.05

−.23**

−.12**

Seeing friends

−.01

.11**

.07*

.03

−.05

−.01

.01

Receiving help

−.01

.05

.04

.02

.04

−.01

.02

Family support

.12**

.09**

.12**

−.04

−.07*

−.08*

−.04

Trust

.16**

.03

.06

−.10**

−.07*

−.08*

−.09*

Fairness

.04

.04

.07*

−.09*

−.10**

−.10**

−.08*

Learning

.05

.13**

.08*

−.04

.02

−.01

.05

Respect

.00

.02

.10**

−.09*

−.02

−.13**

−.08*

Recognition

.05

.00

.02

−.01

−.06

−.05

−.10**

Spirituality

.06*

−.02

.03

−.02

.01

−.01

−.04

R

.47**

.45**

.46**

.40**

.35**

.39**

.36**

Note: aGender is coded into 1 male and 2 female

*p  <  .05; **p  <  .01

The strongest predictor of life satisfaction was health, i.e., better health predicted better life satisfaction. Even after controlling it, income stayed as significant predictor of life satisfaction. Other significant predictors were variables from social domains, such as trust and family support, while spirituality barely reached significance. People, who reported higher trust in community and more family support, as well as importance of religion in their life, reported better life satisfaction. Together, the measured variables accounted for 22% of life satisfaction variance.

Concerning the positive emotion, better health, more family support, more chance to learn new thing and more often meeting friends predicted being happier and more satisfied over the past month. Respect and fairness appeared to be significant but not too strong in predicting satisfaction. People who perceived more fairness in society and more respect from others also reported to be more satisfied. Total explained variance for feeling happy over the past month was 20%, while for feeling satisfied was 21%.

In predicting the experience of negative emotions over the past month, health again showed significant negative betas for all emotions expect for angry feelings. Lower trust in society and feeling that people take advantage of you repeatedly predicted experiencing more negative emotions. Also, less family support and help was related to the experience of feeling depressed and angry. The feeling not being respected was associated with being sad, depressive, and stressed, while perception of not getting the deserved recognition was associated with feeling stressful. Total explained variances of being sad, depressive, stressed, and angry over the past month, by measured variables, were 16%, 15%, 13%, and 12%, respectively.

Discussion

The study explored evaluative and experiential measures of well-being and their correlates with different life domains variables. We examined those research questions on a large, nationally representative sample. Examining people’s well-being at the national-level groups can provide important information to government and policy making organizations in guiding and improving the quality of life in society and specific groups.

Widely used life satisfaction was examined as evaluative measure of well-being as it elicits global evaluation of one’s life. Also, we employed time-based measure of well-being, measuring experience of two positive and four negative emotions2 over the past month. Usually, experienced well-being is concerned with momentary emotions and their experiences in the real time (Kahneman & Krueger, 2006). Due to the nature of our survey, which was conducted at only one time period, respondents were asked to report the experience of certain emotions in the last month. The given time frame reduced the extent of memory bias, as reports are more likely to be anchored in actual experiences than are reports of emotions in general.

Predictors were number of specific items which covered physical (i.e., health), social (i.e., seeing friends, family support, receiving help, trust, fairness), and psychological (i.e., learning, respect, recognition, spirituality) domains, and thus provided insight on respondents’ view of society, relationships, and personal growth.

Concerning the life satisfaction, 61% respondents reported to be very satisfied (ratings of 7 and higher), while on the lower end of spectrum, there were only 8% of them (ratings of satisfaction less than 4). If we borrow the terminology from the Gallup organization (Harter, Arora, & Neftzger, 2008), which they use for the cross-country comparisons, first group (61%) could be considered as “thriving,” which means that they have their basic needs, such as food and shelter, met. Second group (8%) could be considered as “suffering” residents and they are less likely to have their basic needs met. The middle group (31% respondents in our sample) refers to “struggling” residents who tend to have lower income and are much more likely to worry about money on a daily basis (Harter & Gurley, 2008). Asked about their emotions, Croatian citizens reported experiencing significantly more positive emotions, i.e., to be happy and satisfied, than negative ones, i.e., to be depressed, angry, sad, and stressed, over the past month.

Further, we examined what are the predictors of evaluative and experienced measures of well-being, while controlling for possible effects of age, gender, and income. Even after controlling for income, higher income was still significant predictor of better life satisfaction, and in less extent of being more satisfied and less angry over the past month. This is in accordance with previous findings that the country’s income level is positively correlated with ratings of citizens’ global judgments of life satisfaction, while more weakly correlated with experienced feelings over time (Diener, Ng, Harter, & Arora, 2010; Diener & Seligman, 2004; Kahneman et al., 2006). Also, being female was a significant predictor of being sad and depressive over the past month, which is in the line of research that women appear more likely to develop cognitive strategies that are associated with increased susceptibility to depressive states (Nolen-Hoeksema, 2006).

Life satisfaction was best predicted by health variable, i.e., better health was associated with higher ratings of life satisfaction. Health consistently shows a strong relationship with well-being measures, which is reported in many studies and literature reviews ( Dolan et al., 2008; Diener & Seligman, 2004; Lelkes, 2006). Following are the variables from social domain such as trust and family support. Trusting others more and getting more help from family predicted better life satisfaction. Social trust, which is trust in other people, was found in previous research to be associated with higher life satisfaction (Helliwell, 2006). To maintain good well-being, people need supportive and positive family and social relationships (Diener & Seligman, 2004; Lelkes, 2006).

As for positive emotions, better health and more family support predicted happiness and satisfaction over the past month. Kaliterna Lipovčan and Prizmić-Larsen (2006a) found that health is a strong predictor of happiness in the Croatian society. Another two strong predictors were learning new things and seeing friends. In terms of eudaimonic well-being, learning could be seen as variable of self-realization and personal growth (Ryan & Deci, 2001). In the research of Park, Peterson and Seligman (2004), love for learning, defined as one of the character strengths, added little bit to life satisfaction, while it added almost nothing to life satisfaction in our research. However, we found that opportunity to learn new things was associated with positive emotions. People who had more of it felt happier and more satisfied over the past month than ones who did not.

Numerous other studies provide evidence for the positive relationship between social support and well-being (Diener & Seligman, 2004; Dolan et al., 2008; Lelkes, 2006). Some authors emphasized the importance of social relationships in the maintenance of health and well-being, pointing out that social support promotes well-being by influencing cognition and behaviors in the way that promotes positive emotions (Cohen, Gottlieb, & Underwood, 2001).

In predicting the experience of negative emotions, only two predictors, health and family support, were similar to those of positive emotions, both showing negative associations with negative emotions. It confirms again that health and family support are very important predictors for both evaluative and experienced well-being (Diener and Seligman, 2004; Dolan et al., 2008). Additional strong predictors were fairness and social trust, followed by respect and recognition. Perceived quality of society, like trusting one another and being mutually helpful, described in literature as social capital, are usually examined in connection with life satisfaction (Böhnke, 2008; Helliwell, 2006). Having high social capital indicates higher well-being (Diener & Seligman, 2004). In our data, lower perceived social capital was particularly associated with citizens’ experience of negative emotions, i.e., of being more sad, depressed, angry and stressed over the past month. Not being respected was associated with being sad, depressed, and stressed, while not having recognition was associated with more stress. Those variables, which in some ways reflect position of individual in social context and relationships in society, like perceived social actualization and contribution, were also related to experiencing negative emotions.

This study showed that different components of well-being were predicted by similar variables, on one hand, but also has distinct variables predicting it, on the other. Positive and negative emotions demonstrated different relationships to predictors, and again confirmed their independence found in the literature (Lucas et al., 1996). In some way, those experiential responses were strongly related to variables from social and personal domains, than evaluative measure. Positive emotions showed association with personal growth variables and aspects of life more related to individual and his or her growth (i.e., psychological well-being), while negative emotions were related to social domain, like aspects of life related to other people, relationships, and community (i.e., social well-being).

However, there are several limitations of the study, which should be mentioned. The cross-sectional design and the correlative nature of the study do not allow any casual interpretation of relationships between well-being measures and various life domains variables. Since only self-report measures were used, in future research, it would be important to utilize a multimethod approach by including peer reports. Although the life domains variables used were quite diverse, there are still areas of life that we might overlook such as personal safety, leisure, etc. Finally, specific emotions were assessed over the last month, so retrospective component in measuring the affect exists to some extent. If the mood was assessed momentarily or in reference to the previous day, it would reduce the extent of bias in recalling past experiences.

In summary, the most important predictors of life satisfaction were income, health and family support. Distinctive predictors of positive emotions were learning and seeing friends, while for negative emotions, significant predictors were trust, fairness, and recognition. Those results could be valuable tools for government and policy makers in improving well-being of the nation not only through economic changes but also by improving health care, supporting family values, promoting trust and fairness in social climate, and expanding opportunities for learning.

The national accounts of well-being can have broad benefits for society. Targeting specific groups within society can give more detailed answers for applying specific intervention and programs too. As many researchers now point out, governments that effectively promote well-being can produce engaged citizens who influence growth and prosperity of the country and build a happy society (Diener & Seligman, 2004; Diener et al., 2008; Kahneman et al., 2006).

Footnotes

  1. 1.

     The income was calculated in Euro based on currency rate of November 2007 (1 Euro  =  7.32 Croatian Kuna).

  2. 2.

     In the experience sampling studies, it was shown that positive emotions are highly intercorrelated, while the correlations among the negative emotions, still positive, are lower; thus we used more negative than positive emotions (Kahneman & Krueger, 2006).

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Zvjezdana Prizmić-Larsen
    • 1
  • Ljiljana Kaliterna Lipovčan
    • 2
  • Tihana Brkljačić
    • 2
  1. 1.Washington University in St. LouisSt. LouisUSA
  2. 2.Institute of Social Sciences Ivo PilarZagrebCroatia

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