من نمی توانم بدون تو لبخند بزنم: همبستگی همسر در رضایت از زندگی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|37544||2009||15 صفحه PDF||سفارش دهید||10603 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Economic Psychology, Volume 30, Issue 4, August 2009, Pages 675–689
This paper tests whether one partner’s happiness significantly influences the happiness of the other partner. Using 10 waves of the British Household Panel Survey, it utilizes a panel-based GMM methodology to estimate a dynamic model of life satisfaction. The use of the GMM-system estimator corrects for correlated effects of partner’s life satisfaction and solves the problem of measurement error bias. The results show that, for both genders, there is a positive and statistically significant spillover effect of life satisfaction that runs from one partner to the other partner in a couple. The positive bias on the estimated spillover effect coming from assortative mating and shared social environment at cross-section is almost offset by the negative bias coming from systematic measurement errors in the way people report their life satisfaction. Moreover, consistent with the spillover effect model, couple dissolution at t + 1 is negatively correlated with partners’ life satisfaction at t.
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 and 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 and 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.