I can’t smile without you: Spousal correlation in life satisfaction
Introduction
The idea that married people care a great deal about the well-being of their partner is not new to economists (Becker, 1973, Becker, 1974, Friedman, 1986). The past three decades have seen a significant increase in the number of studies showing that people in marriage tend to behave altruistically towards their partner (see, for example, Ermisch, 2003, Foster and Rosenzweig, 2001). However, while it may be possible to make some inferences about the degree of caring between partners from their behaviour, the idea that there may be a direct spillover of well-being from one partner to the other has rarely been tested empirically.
This paper aims to do just that. Using a long-run panel of nationally representative randomly sampled married and cohabiting individuals, it examines the extent of spousal correlation in subjective well-being data, particularly self-rated life satisfaction (LS). It proposes that a positive correlation between partners’ LS may reflect three distinct processes. First, individuals who are born happy or are born with innate predispositions that make them happy may tend to marry those who are similar to them. In addition to this, people of the same family background or life styles – in other words, same unobserved social factors – may also tend to marry each other. This matching of fixed personal characteristics on the marriage market is analogous to the concept of assortative mating (Becker, 1974). Manski (1995) refers to such phenomena as correlated effects of social interactions.
Second, given that marriage allows individuals to share with their partner the kind of physical and emotional resources that may not be available for each person to obtain outside marriage (Waite & Gallagher, 2000), correlated effects may also arise from the shared social environment (which can either be time-invariant or time-variant) that is simultaneously affecting LS for both spouses.
Lastly, the observed correlation may be the result of a direct spillover of LS within the couple. The idea is that, if a husband cares about his wife, then her LS becomes one of the main determinants of his own LS, and vice versa. This generates a possibility that a husband will be ceteris paribus happier when his wife is happier for whatever reasons that make her happy but not him directly. Hence, we would expect an increase in one partner’s LS to be positively correlated with the other partner’s LS even after allowing for all the factors that can affect both partners’ LS at the same time. This phenomenon is likened to the endogenous effects in Manski’s terminology, whereby the individual outcome is a function of group achievement.
In addition to the above confounding influences which make it difficult for the true relationship between partners’ well-being to be identified, the estimates of spousal correlation in LS may also suffer from the negative measurement error bias. There may be, for example, a tendency for individuals to misreport their true LS in surveys. The low signal-to-noise ratio caused by misreporting can result in an estimated coefficient on partner LS that is biased towards zero in a large sample. In short, because there are both positive (correlated effects) and negative (measurement error) biases involved, the direction of bias is unclear on a priori ground.
This paper uses 10 waves of the British Household Panel Survey (BHPS) data to examine the extent of spousal correlation in LS. In particular, it uses the “system GMM estimator” proposed by Arellano and Bover (1995) and Blundell and Bond (1998) to estimate the causal spillover effect that runs from one partner’s life satisfaction to the other partner’s life satisfaction. The use of the GMM-system estimator, which is a unique approach in the study of happiness, control for the correlated effects and solve the problem of measurement error bias in self-rated life satisfaction through instrumentations and first-differencing. The results show that there is strong evidence of a spillover effect of LS, which suggests that well-being is transferable from one partner to the other. Consistent with the spillover effect model, partners’ LS today are also associated with lower probabilities of partners separating or divorcing one period into the future.
There are similarities in terms of research questions and analytic strategy between this paper and previous studies that examined similarities in a husband’s and wife’s behaviour such as smoking (Clark & Etile, 2006), their political preferences (Kan & Heath, 2006), and their sporting activities (Farrell & Shields, 2002).
This article is organised as follows: Section 2 reviews relevant past research on marriage and well-being. Section 3 addresses theoretical issues revolving around the various interpretations of the correlation between partners’ LS. Section 4 describes how to implement a test and the data set. Section 5 discusses the results, and Section 6 concludes.
Section snippets
Marriage, subjective well-being, and spillovers
Previous research on marital status and emotional well-being is clear on one point: married persons are significantly happier and more satisfied with life than those who are divorced, separated, widowed, or single, across various countries and time periods (Gove et al., 1983, Marks and Lambert, 1998, Mastekaasa, 1994). The large psychological benefits of marriage persist even when the selection of happy people into marriage is controlled for in the analysis (Frey and Stutzer, 2006, Mastekaasa,
Theory
In this section, I will briefly discuss the three underlying mechanisms that may account for the raw correlation between a husband’s and wife’s LS levels: assortative mating, shared social environment, and spillover effect.
The utility model of couples
Consider first Gary Becker’s (1974) simple utility function of an individual i in a marriage to individual j at any given time, which can be written aswhere X is a vector of the consumption of commodities within the household, and is the individual i’s partner’s utility. The individual’s utility is assumed to be increasing with X, which is divisible and can be shared between the couple. An increase in X therefore raises the individual’s utility both through a direct effect upon
Life satisfaction spillovers
Table 2 reports results from the GMM-system estimator described in the previous section. The dependent variable is the respondent’s self-rated life satisfaction measured cardinally (on a scale of 1 to 7).
Conclusion
This paper has used 10 waves of BHPS data to study intra-spousal correlations in self-reported life satisfaction data. Its primary objective was to determine whether the observed correlation is due largely to partners’ fixed traits are similar through assortative mating by personality traits on the marriage market, partners sharing the same social environment that simultaneously affects their well-being, or a spillover effect of life satisfaction from one partner to the other.
A simple OLS model
Acknowledgements
I am grateful to Andrew Oswald, Andrew Clark, Paul Dolan, Ian Walker, Robin Naylor, Geeta Kingdon, Rainer Winkelmann, Ulrich Schimmack, Nateecha Ratanadilok Na Bhuket, Anthony Fielding, participants at the Royal Economic Society at Nottingham and the Economics of Happiness Symposium at the University of Southern California in March, 2006, and two anonymous referees for their valuable comments. The British Household Panel Survey data were made available through the UK Data Archive. The data were
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