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Social Capital and Happiness in the United States

Abstract

This paper explores the association between social capital and average happiness in the United States. Social capital is measured as a multidimensional concept consisting of social trust and two different indicators of sociability. In order to employ the variation both over time and across states, the data are organized in either a panel of nine US Census regions over the period 1983–1998 or in averages over this period in a cross-section of 48 states. The results show that social trust is positively associated with happiness while the potential effects of informal sociability at the level of society only appear significant in the regional estimates. The findings document the importance of social trust for average happiness but also hold more general implications for social capital theory.

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Fig. 1

Notes

  1. Here, it is important to note that even while the average state has more than 1,000 observations, the number of respondents forming the state average is distributed between as little as 102 in Wyoming and 5427 in California. In addition, even though a number of other factors may be captured in the Needham data, I only include questions that have been asked in all years from 1983 to 1998.

  2. While the use of the approximate middle of the period between 1983 and 1998 from which data are derived may be intuitively appealing, it might nonetheless induce unnecessary noise if relative state GDP changed across the period. This is in general not the case, and the results in the following are stable to using GDP data from the beginning of the period or the average across 1983–1998.

  3. By using these data, I avoid one of the pitfalls of international corruption conviction rates, which arises from the fact that more corruption goes undetected in countries with weak institutions. As the US data available from this source report the conviction rate resulting from federal investigations, the potentially counteracting effects of cross-state institutional differences do not bias the data.

  4. Instrumental variables (IV) regression is a standard technique for separating causal influences in economics, although other fields still regard it with some scepticism. The identification of a causal influence in IV estimates, instead of a partial correlation that could reflect an influence from A to B, B to A, or both, rests on finding a set of variables that are associated with A but not B. The technique therefore employs these variables to predict A in a first stage, and the predicted values are used in a second stage regression explaining B. As such, the variation in the predicted values of A cannot logically stem from an influence from B to A. If the estimate of A in the second stage is nonetheless significant, this can be taken as a relatively safe indication that the causality runs from A to B, given that the instruments are valid (not correlated with the unexplained part of B).

  5. The happiness and life satisfaction data can be found at Ruut Veenhoven’s World Database of Happiness (http://www1.eur.nl/fsw/happiness/) while the combined trust data from the World Values Survey, the LatinoBarometro, the AfroBarometer and the Danish Social Capital Project can be obtained from the author.

  6. When considering the result that corruption is not significant, it is worth noting that a set of analyses in an early version of this paper indicated that corruption, which is used as a proxy for the quality of formal institutions, may be positively associated with the happiness of American men, but not women. As such, the data may provide qualified support for Frey and Stutzer’s (2002) result that institutions, defined in a broad sense, matter.

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Acknowledgement

Christina Bjerg provided excellent research assistance. I am grateful to Andrew Clark, Justina Fischer and two anonymous referees for providing valuable comments on earlier versions of the paper. Needless to say, I am solely responsible for any remaining errors.

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Correspondence to Christian Bjørnskov.

Appendix

Appendix

Table 5 Social capital, income and happiness, 1983–1998

Constructing the Social Activity Factors

To construct the two sociability indices, I employ the individual-level data on how frequently within the last 12 months an individual has participated in one of 20 different activities. These activities include: bowling, camping, playing cards, attending church or other place of worship, going to a classical concert, going to a pop or rock concert, going clothes shopping, going to a club meeting, working on a community project, giving or attending a dinner party, going out to dinner at a restaurant, entertaining people in ones own home, sending a greeting card, attending a lecture, going to the movies, going on a picnic, attending a sporting event, going swimming, playing tennis, and visiting an art gallery or museum. Following Putnam’s (2000) findings and theoretical notion of social capital, one would expect that they all loaded onto a single component in the principal components analysis used here to construct the indices. Yet, as Table 6 shows, the data form two distinct dimensions, which clearly adds important nuance to Putnam’s concept. Five principal components have eigenvalues above one, but a standard scree plot shows a clear elbow at two components, which is the solution chosen in this paper.

Table 6 Principal components analysis of sociability

With respect to the interpretation of the components, the picture may at first seem confusing, as is often the case when using principal components analysis. However, the variables loading onto the first component seem to share the characteristic that the activities are to some extent formal and organized. The remaining variables loading onto the second component, on the other hand, capture social activities that are less formally organized and that are not necessarily planned in advance, e.g. going swimming or shopping. This interpretation is further sustained by noticing that the only variable loading onto both components—giving or attending dinner parties—can reflect both formal and informal social contact.

Happiness Determinants at the Individual Level

While the DDB Needham (2007) data do not form a panel at the individual level, it provides a large set of observations on individual happiness and other variables from 1983 to 1998. Table 7 provides an ordered probit analysis of the determinants of happiness at the individual level, taking into account income, age, education, religiosity (measured by the frequency of attendance), employment status and civil status, following the set of robust determinants in Bjørnskov et al. (2008). This analysis also includes the three social capital variables: social trust, informal sociability and formal sociability.

Table 7 Individual-level results

The analysis of the Needham data exhibits the standard findings. Income and religiosity are positively associated with happiness while higher education in general is negatively associated as is being without a spouse (for several reasons) and age shows a curvilinear relation with the lowest point at approximately 42 years. As is also standard in developed countries, gender is not associated with happiness. Finally, the three social capital variables all exhibit strongly positive associations with individual happiness, a set of findings that is consistent with the broader social capital literature (Putnam 2000, 2001; Uslaner 2002; Bjørnskov 2003). As such, there is evidence of effects of both social trust as argued in Uslaner (2002) and of sociability as in Winkelman (2006).

The main purpose of the individual level analysis, however, is to take out most of the individual-level effects of social capital in a rather simple way. As such, I calculate the residuals from the individual-level regressions and aggregate them annually at the cross-regional level or across the full period 1983–1998 at the state level. Across time, the raw happiness data and these residuals correlate at 0.90, at the regional level, the correlation is 0.87, ranging from 0.83 to 0.96, while the correlation at the cross-state level is 0.71. In principle, the control of individual-level effects may therefore make a difference, which is explored in Tables 3 and 4 in the main text.

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Bjørnskov, C. Social Capital and Happiness in the United States. Applied Research Quality Life 3, 43–62 (2008). https://doi.org/10.1007/s11482-008-9046-6

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Keywords

  • Happiness
  • Life satisfaction
  • Social capital
  • Trust
  • United States