Residents of Poor Nations Have a Greater Sense of Meaning in Life Than Residents of Wealthy Nations

Abstract

Using Gallup World Poll data, we examined the role of societal wealth for meaning in life across 132 nations. Although life satisfaction was substantially higher in wealthy nations than in poor nations, meaning in life was higher in poor nations than in wealthy nations. In part, meaning in life was higher in poor nations because people in those nations were more religious. The mediating role of religiosity remained significant after we controlled for potential third variables, such as education, fertility rate, and individualism. As Frankl (1963) stated in Man’s Search for Meaning, it appears that meaning can be attained even under objectively dire living conditions, and religiosity plays an important role in this search.

Keywords

meaning in life life satisfaction wealth religion society well-being

Meaning in life is characterized by a system that connects specific, often fragmented everyday experiences with a general, coherent whole (Baumeister, 1991). Not surprisingly, meaning in life is considered an important aspect of well-being (King & Napa, 1998; Steger, Frazier, Oishi, & Kaler, 2006). Although there have been many large-scale surveys on subjective well-being (investigating such variables as life satisfaction, positive affect, negative affect; see Oishi, 2012, for a review), meaning in life has not been examined in a large international survey. Thus, researchers know virtually nothing about cross-national differences in this variable (see Steger, Kawabata, Shimai, & Otake, 2008, for a comparison between the United States and Japan).

Several studies in the United States have shown that individuals who report higher levels of life satisfaction and positive affect also report having more meaning in life (e.g., Baumeister, Vohs, Aaker, & Garbinsky, in press; King, Hicks, Krull, & Del Gaiso, 2006; Steger et al., 2006). If the individual-level findings from North America can be generalized to the level of society, then people living in wealthy nations should report having more meaning in life than those living in poor nations because people in wealthy nations typically report higher life satisfaction (Oishi, 2012).

However, the relation between wealth and meaning in life could be very different from the relation between wealth and life satisfaction at the societal level. Several modernization theorists have speculated that people living in wealthy, modern societies are more likely to suffer from a lack of meaning in life than those living in premodern societies. For instance, philosopher Charles Taylor (1989) argued that “the question of what makes human life worth living or what confers meaning on [people’s] individual lives . . . is an essentially modern predicament” (p. 10). Similarly, Baumeister (1991) started his book Meanings of Life as follows:

Desperate people do not ponder the meaning of life. When survival is at stake . . . life’s meaning is irrelevant. The meaning of life is a problem for people who are not desperate, people who can count on survival, comfort, security, and some measure of pleasure. (p. 3)

Before the modern era, people were more often desperate to survive. They did not ponder the meaning of life; rather, religion provided them with ready-made answers to life’s biggest questions. Much like people in the premodern era, modern people living in poor nations are more religious than modern people living in wealthy nations (Barro & McCleary, 2003). Instead of relying on religion to give life meaning, people in wealthy societies today try to create their own meaning via their identity and self-knowledge. Baumeister argues that this is what makes it difficult to have a stable sense of meaning in life in wealthy modern societies. He states that “creating the meaning of your own life sounds very nice as an ideal, but in reality it may be impossible” (p. 6). However, if religion provides people with a purpose and a coherent sense of meaning in their lives, people living in poor nations might report having meaning in life more often than people in wealthy nations.

Using data from the Gallup World Poll, we examined the relation between societal wealth and meaning in life, and we explored the role of religiosity as a mediator between societal wealth and meaning in life. Both meaning in life and religiosity are complex concepts. Yet the Gallup polls assessed them with one item each. Thus, we first tested whether self-reports of meaning in life and religiosity were associated with objective indicators, such as suicide rate and fertility rate, that should be associated with them. We expected that meaning in life and religiosity should be inversely associated with suicide rate (Baumeister, 1990; Durkheim, 1897/1951; Edwards & Holden, 2001; Stack, 1983), whereas they should be positively associated with fertility rate (Hayford & Morgan, 2008; Umberson & Gove, 1989).

After establishing the validity of self-reports of meaning in life and religiosity, we tested the main research question: whether religiosity mediates the link between societal wealth and meaning in life. In addition, we tested four alternative accounts for the link between societal wealth and meaning in life: education (wealth → more education, more critical thinking → less meaning), the number of children per household (wealth → less children → less meaning), social support (wealth → less social support → less meaning), and individualism (wealth → more individualism → less meaning). Finally, we tested the mediation model using multilevel models to control for respondents’ attributes, such as age, gender, and marital status. In sum, we report one of the first comprehensive tests of the association between societal wealth and meaning in life.

Method

Gallup conducted a survey in 132 nations in 2007. There were 141,738 respondents (65,830 men; 75,883 women; 25 did not report). Meaning in life and religiosity were assessed with the following items: “Do you feel your life has an important purpose or meaning?” and “Is religion an important part of your daily life?” (yes/no). Life satisfaction was assessed with an item asking respondents to indicate where their current life stands on a ladder scale, ranging from 0 (worst possible life) to 10 (best possible life).

Suicide rates were taken from the 2011 World Health Organization (WHO) reports. Because the WHO reports the suicide rate for men and women separately, we computed the overall suicide rate for each nation by taking the mean of the rates for men and women. Suicide data were available for 84 nations. The 2007 data on gross domestic product (GDP) per capita for each nation were taken from the World Development Indicators online database (World Bank, 2009). GDP per capita was available from 131 nations. We log-transformed GDP per capita. Education data were taken from the 2007 International Human Development Index’s Education subscale (United Nations Development Programme, 2011), which is a composite of the years of education that adults have completed and the expected years of education for children. Education data were available for 127 nations.

We were unable to obtain direct information on the number of children per household. The closest information we were able to find was the total fertility rate, which is the average number of children that would be born per woman if all women lived to the end of their childbearing years and bore children according to a given fertility rate at each age (Central Intelligence Agency, 2011). Total fertility data were available for 128 nations. The 2007 Gallup World Poll included one item on social support: “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not? Yes or no?” Social support data were available for 131 nations. Finally, individualism scores were obtained from the Hofstede Centre (http://geert-hofstede.com/countries.html) for 75 nations.

Results

Nation-level analysis

For the sake of clarity, first we present the data analyzed at the nation level. We examined the validity of self-reported meaning in life and religiosity. As predicted, nations higher in self-reported meaning in life had lower suicide rates, r(80) = −.44, p < .001. Consistent with the findings of Durkheim (1897/1951), our results showed that more religious nations had lower suicide rates, r(81) = −.52, p < .001. Also, as predicted, nations with high levels of self-reported meaning in life and religiosity also had high fertility rates (more children), r(123) = .51, p < .001, and r(124) = .59, p < .001, respectively. Thus, self-reports of meaning in life and religiosity showed a certain degree of validity.

Next, we tested our main research question. Although life satisfaction was highly positively correlated with GDP per capita, r(129) = .84, p < .001, meaning in life was negatively associated with GDP per capita, r(127) = −.49, p < .001. That is, residents of wealthy nations reported lower levels of meaning in life than did those of poor nations (Fig. 1). Of note, wealthy nations also had higher suicide rates than poor nations did, r(82) = .28, p = .01 (Table 1). We next tested whether self-reports of meaning in life would explain the link between GDP per capita and suicide, using Mplus (Version 4.21; Muthén & Muthén, 2006) with bootstrap replacements set to 10,000. As predicted, the link between GDP per capita and suicide rates was mediated by meaning in life, indirect effect = 1.03; 95% confidence interval (CI) = [0.468, 1.76], z = 3.14, p = .002. Finally, we tested a path model (Fig. 2). In the path model, GDP per capita was associated with less religiosity, which meant less meaning in life, and less meaning meant higher suicide rates, indirect effect = 0.874, 95% CI = [0.56, 1.21], z = 3.22, p < .001. These analyses demonstrated that self-reports of meaning in life and religiosity were able to explain the association between societal wealth and suicide rates, providing an additional piece of evidence for the validity of self-reports of meaning in life and religiosity.

Fig. 1.

Scatter plot (with best-fitting regression line) showing the association between the wealth of nations and the proportion of residents who said they have meaning in life. National wealth was assessed via log-transformed 2007 gross domestic product (GDP) per capita data. For purposes of readability, only key countries are labeled. N.Z. = New Zealand, U.A.E. = United Arab Emirates, U.K. = United Kingdom, U.S. = United States.

Table 1.

Nation-Level Descriptive Statistics for and Correlations Among Key Variables

		Correlations
Variable	M	2	3	4	5	6	7	8	9
1. Life satisfaction	5.52 (1.12)	−.33**	.84**	−.50**	.02	.70**	−.58**	.74**	.57**
2. Meaning in life	.91 (.08)	—	−.49**	.63**	−.44**	−.50**	.51**	−.25**	−.37**
3. GDP per capita	8.28 (1.65)		—	−.66**	.28*	.84**	−.72**	.69**	.64**
4. Religion	.71 (.24)			—	−.52**	−.75**	.59**	−.50**	−.61**
5. Suicides per 100,000 residents	11.00 (8.47)				—	.47**	−.50**	.18	.20
6. Education	.65 (.21)					—	−.82**	.63**	.68**
7. Fertility	2.61 (1.44)						—	−.56**	−.31**
8. Social support	.85 (.10)							—	.57**
9. Individualism	40.56 (23.45)								—

Note: Standard deviations are given in parentheses. The mean for gross domestic product (GDP) per capita was obtained by log-linear transforming 2007 GDP per capita data. “Religion” denotes the proportion of residents who said “yes” to the question “Is religion an important part of your daily life?” “Meaning in life” denotes the percentage of residents who said “yes” to the question “Do you feel your life has an important purpose or meaning?” Education ratings were drawn from the United Nations Human Development Index’s Education subscale (scores range from 0 to 1.00). “Fertility” indicates the total fertility rate (number of children born per woman who has reached childbearing age). “Social support” indicates the proportion of residents in the 2007 Gallup World Poll who said “yes” to the social support item. The individualism score was taken from The Hofstede Centre’s international studies (http://geert-hofstede.com/countries.html).

p < .05. **p < .01.

Fig. 2.

Path model showing the influence of the wealth of nations on suicide rates, as mediated by religiosity and meaning in life. National wealth was assessed via log-transformed 2007 gross domestic product (GDP) per capita data. Asterisks indicate significant paths (**p < .01). CI = confidence interval.

After establishing that self-reports of meaning in life and religiosity had a reasonable level of validity, we went on to test the main mediation model: whether religiosity mediated the inverse association between societal wealth and meaning in life. As expected, religiosity was positively associated with meaning in life and negatively associated with GDP per capita (Table 1). A multiple regression analysis regressing meaning in life on GDP per capita and religiosity revealed that once religiosity was included, GDP per capita was no longer associated with meaning in life, b = −0.006, SE = 0.004, β = −0.13, t(125) = −1.44, p = .15. Religiosity remained a significant predictor of meaning in life, b = 0.18, SE = 0.03, β = 0.54, t(125) = 5.90, p < .001 (Table 2). A mediation analysis using Mplus 4.21 with the bootstrap replacement set to 10,000 showed a significant mediation effect, indirect effect = −0.017, SE = 0.004, 95% CI = [–0.025, –0.010], z = −4.64, p < .001. That is, (a) wealthy nations were less religious than poor nations, (b) religious nations had higher meaning in life than nonreligious nations, and (c) religiosity partially accounted for the difference between wealthy and poor nations in meaning in life.

Table 2.

Multiple Regression Results at the Level of Nation, Regressing Meaning in Life on Gross Domestic Product (GDP) per Capita, Religiosity, and Potential Third Variables

Model and predictor	b	β	t	p
Model 1: R²(126) = .241
Constant	1.10 (0.031)	—	t(126) = 35.57	< .001
GDP per capita	−0.023 (0.004)	−0.491	t(126) = −6.32	< .001
Model 2: R²(125) = .406
Constant	0.835 (0.053)	—	t(125) = 15.89	< .001
GDP per capita	−0.006 (0.004)	−0.132	t(125) = −1.44	.153
Religiosity	0.175 (0.030)	0.542	t(125) = 5.90	< .001
Model 3: R²(120) = .420
Constant	0.831 (0.055)	—	t(120) = 15.07	< .001
GDP per capita	−0.010 (0.006)	−0.209	t(120) = −1.613	.109
Religiosity	0.186 (0.034)	0.576	t(120) = 5.446	< .001
Education	0.040 (0.055)	0.106	t(120) = 0.729	.467
Model 4: R²(120) = .442
Constant	0.777 (0.063)	—	t(120) = 12.40	< .001
GDP per capita	−0.002 (0.005)	−0.036	t(120) = −0.326	.745
Religiosity	0.170 (0.030)	0.523	t(120) = 5.668	< .001
Fertility	0.009 (0.006)	0.172	t(120) = 1.702	.091
Model 5: R²(124) = .435
Constant	0.737 (0.065)	—	t(124) = 11.43	< .001
GDP per capita	−0.013 (0.005)	−0.286	t(124) = −2.636	.010
Religiosity	0.182 (0.029)	0.562	t(124) = 6.225	< .001
Social support	0.181 (0.071)	0.240	t(124) = 2.535	.012
Model 6: R²(68) = .385
Constant	0.827 (0.090)	—	t(68) = 9.156	< .001
GDP per capita	−0.007 (0.008)	−0.110	t(68) = −0.803	.425
Religiosity	0.186 (0.047)	0.542	t(68) = 3.973	< .001
Individualism	−0.000 (0.001)	−0.001	t(68) = −0.007	.995

Note: Standard errors are given in parentheses. In each model, religiosity was the main predictor, and the other variables shown were control variables.

Although our hypothesis was supported, the inverse association between societal wealth and meaning in life could be explained by other variables. First, education could explain the inverse link between societal wealth and meaning in life as follows: wealth → more education, more critical thinking → less meaning. Indeed, education was substantially higher in wealthy nations than in poor nations, r(125) = .84, p < .001, and meaning in life was lower in nations with higher education, r(122) = −.50, p < .001. Second, the number of children per household could explain the same link as follows: wealth → less children → less meaning. Indeed, fertility rates were substantially lower in wealthy than in poor nations, r(125) = −.72, p < .001, and meaning in life was also lower in nations with low fertility (fewer children) than in nations with high fertility (more children), r(123) = .51, p < .001. Third, the inverse association between societal wealth and meaning in life could be explained by individualism as follows: wealth → more individualism → less meaning. Indeed, individualism was substantially higher in wealthy nations than in poor nations, r(73) = .64, p < .001, and meaning in life was lower in nations with higher levels of individualism, r(71) = −.37, p = .001.

Fourth, the link between more wealth and less meaning could be explained by social support as follows: wealth → less social support → less meaning. This was not the case, however, as social support was negatively associated with both meaning in life and religiosity,¹ whereas it was positively associated with GDP per capita (Table 1). Finally, the three potential third variables were also associated with religiosity. Education levels and individualism were lower in religious than in nonreligious nations, r(123) = −.75, p < .001, r(72) = −.61, p < .001, whereas fertility was higher in religious than in nonreligious nations, r(124) = .59, p < .001. Thus, the inverse relation between GDP per capita and meaning in life and the mediating role of religiosity could be explained by education, fertility, or individualism.

To test these alternative explanations, we conducted a series of multiple regressions, regressing meaning in life on GDP per capita, religiosity, as well as each of the third variables (Table 2). These analyses showed that religiosity remained a significant predictor of meaning in life, and the inverse association between GDP per capita and meaning in life could not be explained by the level of education, fertility rate, social support, or individualism above and beyond religiosity.

Multilevel analysis

After the test of the four alternative accounts identified religiosity as the most plausible mediator, we went on to a series of multilevel analyses. Although the nation-level analysis showed a clear relation between GDP per capita and meaning in life, aggregating individual responses neglects within-nations variation. In addition, the nation-level analysis did not allow us to control for the individual-level variables that are known to be associated with people’s sense of meaning in life, such as age, gender, and marital status (Steger, Oishi, & Kashdan, 2009).

Thus, we conducted a multilevel mediation analysis using Mplus 4.21. Figure 3 shows the model. At Level 1, meaning in life was regressed on participants’ gender (0 = female, 1 = male), age (grand mean-centered), and marital status (0 = not married, 1 = married). At Level 2, national mean meaning in life (adjusted for gender, age, and marital status) was regressed on GDP per capita and mean religiosity. Furthermore, at Level 2, mean religiosity was regressed on GDP per capita to test the mediation effect.

Fig. 3.

Multilevel mediation analysis model showing the cross-level association between the wealth of nations and meaning in life, as mediated by religiosity. National wealth was assessed via log-transformed 2007 gross domestic product (GDP) per capita data, and meaning in life was adjusted for gender, age, and marital status.

Consistent with the nation-level analysis, the multilevel mediation analysis showed that the direct effect of GDP per capita on meaning in life was mediated by mean religiosity, indirect effect = −0.881, SE = .166, 95% CI = [–1.207, –0.555], z = −5.30, p < .001. Once religiosity was entered as a Level 2 predictor, the direct effect of GDP on meaning in life disappeared, b = −0.005, SE = 0.004, z = −1.30, p = .19.

We next modified the model slightly, this time using individual religiosity (Level 1), as opposed to national mean religiosity (Level 2). The rest of the model was identical to that used in the first multilevel analysis. The mediation effect of religiosity on the link between GDP per capita and meaning in life was again significant, indirect effect = −0.005, SE = .001, 95% CI = [–0.006, –0.003], z = −6.17, p < .001. Whereas the direct effect of GDP per capita on meaning in life was no longer significant in the prior models, the effect of GDP per capita remained significant in this model, b = −0.02, SE = 0.004, z = −5.58, p < .001. That is, even when we included religiosity as a predictor of meaning in life at the individual level, the effect of GDP per capita on individual respondents’ meaning in life was still significantly negative. If two individuals were equally religious, those living in wealthy nations were less likely to report having meaning and purpose in their lives than those living in poor nations.

Discussion

In the present study, residents of poor nations reported more meaning in life than those of wealthy nations, a finding consistent with modernization theory (Baumeister, 1991; Taylor, 1989). Because it is unlikely that lower levels of meaning in life would cause greater societal wealth, we interpret the relation as going from wealth to lower meaning in life. We found that religiosity mediated the negative effect of wealth on meaning in life. As society becomes wealthier, religion becomes less central to people’s life. As religion becomes less central to people’s life, more people lose a sense of meaning in life.

There was also support for the education account: Wealthier nations were more educated, and more educated nations reported less meaning in life. There was also support for the fertility account: Wealthier nations had less children, and having less children was related to lower meaning in life. Likewise, the wealthier the nations were, the more individualistic they were, and more individualistic nations had less meaning in life. Of note, however, religiosity remained a strong predictor of meaning in life, above and beyond education, the number of children, and individualism (Table 2). We also found that residents of wealthier nations had more suicides per 100,000 residents than residents of poor nations did. As expected, the effect of wealth on suicide rates (again, reverse causality is unlikely; high suicide rate → more wealth?) was explained in part by self-reported meaning in life.

These findings have several theoretical and practical implications. First, prior research has repeatedly found that life satisfaction is substantially higher in nations with modern conveniences than in nations without modern conveniences, such as electricity, telephones, TVs, and computers (Diener, Kahneman, Tov, & Arora, 2009). Thus, life satisfaction is associated with objective living conditions, particularly when assessed by Cantril’s self-anchor scale. In contrast, our findings showed that meaning in life is not associated with objective living conditions. For instance, many residents of Niger, Sierra Leone, Togo, and Ethiopia live in difficult economic and political conditions. Indeed, residents of these nations report that their lives are far from ideal (Oishi, 2012). Yet the overwhelming majority of residents in poor nations report having an important purpose or meaning in life, as Figure 1 shows.

Why might this be the case? One possibility is that people can construct meaning from negative events and difficult life circumstances (Frankl, 1963; King & Hicks, 2009; Stephens, Fryberg, Markus, & Hamedani, 2012), and therefore, people in poor nations can find meaning in difficult life circumstances. The second possibility is that in difficult economic conditions, many people are forced to work day and night. By necessity, they are preoccupied with the things that they must do to survive. Under such conditions, they might have a clear sense of meaning in life (e.g., do what I need to survive). As Figure 1 shows, however, there is a great deal of variation in meaning in life even among nations at the lower end of GDP per capita. For instance, Haiti, Yemen, and Senegal are similarly poor, yet Haitians were much less likely to report having meaning in life than people in Yemen and Senegal. Consistent with our hypothesis, results showed that Haitians are less religious than Yemeni and Senegalese (78% of Haitians say religion is important vs. 96% of Yemeni and 99% of Senegalese). Similarly, when we examine the higher ends of GDP per capita, residents of religious nations, such as the United Arab Emirates, Kuwait, United States, and Ireland, report much higher levels of meaning in life than residents of Japan, Hong Kong, and France, which are less religious nations. Thus, it appears that religiosity plays an important role in understanding the national mean levels of meaning in life.

But why should religion provide greater meaning in life? There are several aspects of religion that could give rise to a greater sense of meaning in life. One is a social aspect. Religious people are more likely than nonreligious people to have a stable social network to rely on and socialize with (Myers, 2000). However, this does not seem to be an explanation in the current study, as availability of social support and meaning in life were negatively associated with each other (Table 1). We also checked whether religion was confounded with the number of children per family, as residents of religious nations are more likely to have more children, and in general, having children provides parents with greater meaning in life (Baumeister, 1991; Umberson & Gove, 1989; but see Kushlev, Dunn, & Ashton-James, 2012). As shown above, religiosity remained a strong predictor of meaning in life above and beyond the total fertility rate. Thus, the link between religiosity and meaning in life cannot be explained by the greater number of children in religious relative to nonreligious nations.

It appears then, as Baumeister (1991) speculated, that a central reason for the link between religiosity and meaning in life is that religion gives a system that connects daily experiences with the coherent whole and a general structure to one’s life (see also Gebauer, Nehrlich, Sedikides, & Neberich, 2013). As shown by Stephens and colleagues (2012), religion plays a critical role in constructing meaning out of extreme hardship (see also Diener, Fujita, Tay, & Biswas-Diener, 2012).

Before conclusions are drawn, it is important to acknowledge several limitations of this study. First, there might be other confounds causing the negative association between societal wealth and meaning in life. For instance, societal wealth might make people more materialistic (Vohs, Mead, & Goode, 2006), and an emphasis on monetary and material accumulation might in turn make it more difficult for people in wealthy nations to see what their lives ultimately add up to. It is critical to examine the role of materialism in the relation between societal wealth and meaning in life in the future.

Second, our study was cross-sectional. It is important to conduct a longitudinal study to test whether economic growth gives rise to less religiosity and less meaning in life. Third, meaning in life and religiosity were assessed with a single item, respectively. The self-reports of meaning in life and religiosity showed the predicted associations with suicide rate and fertility. However, both constructs are very complex. For instance, meaning in life typically encompasses coherence, goal-directedness, transcendence, and positive affect, among other factors (Heintzelman, Trent, & King, 2013; Steger et al., 2006). It is possible that people in different nations interpreted the meaning question differently. It is important to replicate the current findings with more comprehensive measures of meaning in life and religiosity after establishing the cross-cultural equivalence of meaning and religiosity scales.

Despite some limitations, this study showed for the first time which nations have higher levels of meaning in life and why. It is noteworthy that meaning in life predicted the suicide rate, whereas life satisfaction did not. This finding has important policy implications. If a government wants to increase its residents’ life satisfaction, then improving economic conditions is critical (Diener, Tay, & Oishi, 2013). In contrast, if a government wants to increase its residents’ meaning in life and prevent suicide, then improving economic prosperity does not seem to help achieve these goals. So far, the discussion of improving societal well-being has centered on economic policies. However, it is critical to distinguish different kinds of well-being, as the predictors of life satisfaction are very different from those of meaning in life at the level of nations.

Footnotes

Acknowledgements

We thank the Gallup organization for making the data available to us. We also thank Jordan Axt, Matt Motyl, and Thomas Talhelm for helpful comments.

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Notes

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