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Is Happiness Contagious? Separating Spillover Externalities from the Group-Level Social Context


We investigate whether individuals feel happier when others around them are happier in broadly defined worker groups. This will be a formal test of spillovers in happiness. Answering this question requires a careful handling of the reflection problem, as it may not be possible to separate the endogenous spillover effects from contextual effects unless an appropriately designed identification strategy is employed. Implementing such a strategy and using the 2008 release of the British Housing Panel Survey, we show that the group-level happiness does not have a statistically significant endogenous effect on individual-level happiness in the Great Britain. We report, however, statistically significant contextual effects in various dimensions including age, education, employer status, and health. These results suggest that higher group-level happiness does not spill over to the individual level in neither negative nor positive sense, while the individual-level happiness is instead determined by social context (i.e., the group-level counterparts of certain observed covariates). We also test the relevance of the “Easterlin paradox” and find that our result regarding the effect of income on happiness—controlling for social interactions effects—is the group-level analogue of Easterlin’s original results.

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  1. The term “ecological settings” (or “social ecologies”) is introduced by developmental psychologists to bridge the gap between behavioral models that focus only on individual-level settings and those that focus only on macro-level settings. As it is originally defined by Bronfenbrenner (1974, 1979), a social ecology is a small enough setting that can capture the idiosyncratic aspects of a person’s life and a large enough setting that can capture the collective interactions a person is exposed to in his/her immediate surroundings—i.e., family, workplace, local labor market, neighborhood, school, etc.

  2. This is also an economically meaningful question, because recent studies suggest that subjective well-being is positively correlated with labor productivity (Boeckerman and Ilmakunnas 2012; Oswald et al. 2013). If there exist significant social interactions effects and if happiness is a determinant of productivity, then the productivity of the worker is determined not only by her own happiness, but the aggregate happiness level in the environment she is associated with.

  3. The BHPS records the date of each interview as day-month-year, allowing us to observe the day-of-the-week on which the interview occurs.

  4. See Taylor (2006), Akay and Martinsson (2009), and Helliwell and Wang (2013) for recent studies.

  5. They use the Framingham Heart Study social network to examine longitudinal interactions in small groups. “Ego” is the person whose behavior is being analyzed, while “alter,” namely the reference group, is the person who is potentially influencing the behavior of the ego.

  6. For example, suppose that there are two groups in the society: group 1 and group 2. Group 1 consists of happier individuals, on average, than group 2. Suppose also that the average education level in group 1 is higher than that in group 2. Is it really the case that individuals in group 1 feel happier because they live in a group consisting of happier individuals, on average? Is it the case that individuals in group 1 feel happier because they live in a group consisting of more educated individuals, on average? Or, is it the case that the society is hit by a shock (say, a policy change) that is perceived more positively by the educated individuals, therefore, the average happiness in group 1 is larger than that in group 2? The first question is related to endogenous effects (i.e., contagion), the second is related to contextual effects, and the third is related to correlated effects.

  7. For example, the mean paternal education in the reference group may increase the happiness directly (both in ex ante and ex post terms), because more educated fathers may have bequeathed a higher wealth, a better neighborhood, or superior education opportunities.

  8. We exclude the Northern Ireland due to clustering issues (see BHPS, Volume A, 2–5).

  9. An alternative peer group selection strategy would be to use occupation groups rather than industries. But, it is well-known that occupation-level groupings may create extra noise in large datasets. We follow the conventional wisdom and use the industry-region combinations in formulating our reference groups.

  10. Due to a potential measurement problem in Wave-1 (Rose 1999), we drop Wave-1 and use the data from Wave-2 to Wave-18 in our empirical analysis.

  11. Until wave-11, for the original BHPS sample, the weight is “wLRWGHT.” Afterwards, for all samples, “wLRWTUK1” is used. Our analysis also takes into consideration the complex survey design (see BHPS, Volume A, 2–5).

  12. See Brock and Durlauf (2001b) and Soetevent (2006) for a detailed survey of the related empirical and theoretical literatures. See also Moffitt (2001).

  13. There are clear-cut results to identify social interactions within binary (Brock and Durlauf 2001a) or multinomial (Brock and Durlauf 2002) discrete choice models. Whether ordered-choice models with social interactions can be identified econometrically or not is an open question in the literature. For theoretical attempts to model social interactions within an ordered-choice framework, see Aradillas-Lopez (2011) and Tumen (2011).

  14. See Tumen and Zeydanli (2013b) for a particular non-linear model that can guarantee identification under certain assumptions.

  15. Most studies in the literature do not pay attention to this factor. See our companion paper, Tumen and Zeydanli (2013a), for the day-of-the-week effect estimates accounting for selectivity.

  16. Standard STATA packages are used in all estimations. Further details on the calculation procedures are available from the authors upon request.

  17. In our ongoing work (see Tumen and Zeydanli 2013b), we perform a similar analysis for job satisfaction; that is, we separate behavioral spillovers from contextual effects for the BHPS measure of job satisfaction. Contrary to the present paper, we capture significant social interactions in job satisfaction using the same data and a similar empirical framework. This suggests that employed workers care about whether workers in their reference groups are satisfied jobwise or not rather than whether they are happy or not. This maybe due to the profound conceptual differences between job satisfaction and happiness. Since we explicitly focus on employed workers in both papers, we conclude that “overall happiness of the employed” does not refer to “job satisfaction.”

  18. See Kahneman and Krueger (2006) for an excellent discussion on the potential links between the classical utility theory and self-reported happiness scores. See Bertrand and Mullainathan (2001) and Ravallion and Lokshin (2001) for skeptical views.

  19. If happiness is positively correlated with productivity, then it is possible to conclude further that workers are more productive in a group of older (or more experienced) workers.

  20. See, for example, Easterlin (1974), van de Stadt et al. (1985), Clark and Oswald (1996), and Ferrer-i-Carbonell (2005).


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Correspondence to Semih Tumen.

Additional information

We thank Andrew Clark, Antonella Delle Fave, Stephanie Rossouw, Claudia Senik, and two anonymous referees for very helpful comments and suggestions. Tugba Zeydanli acknowledges financial support from the European Doctorate in Economics—Erasmus Mundus. The views expressed here are of our own and do not necessarily reflect those of the Central Bank of the Republic of Turkey.


Appendix 1: GHQ Questionnaire

A series of questions are asked in the General Health Questionnaire (GHQ). These questions are:

Have you recently:

  1. 1.

    Been able to concentrate on whatever you are doing?

  2. 2.

    Lost much sleep over worry?

  3. 3.

    Felt that you are playing a useful part in things?

  4. 4.

    Felt capable of making decisions about things?

  5. 5.

    Felt constantly under strain?

  6. 6.

    Felt you couldn’t overcome your difficulties?

  7. 7.

    Been able to enjoy your normal day to day activities?

  8. 8.

    Been able to face up to your problems?

  9. 9.

    Been feeling unhappy and depressed?

  10. 10.

    Been losing confidence in yourself?

  11. 11.

    Been thinking of yourself as a worthless person?

  12. 12.

    Been feeling reasonably happy all things considered?

Answers are coded on a four-point scale from “Disagree strongly” (coded 1) to “Agree strongly” (coded 4)—questions 1, 3, 4, 7, 8, and 12 are coded in reverse order—and added together to provide a total GHQ score of mental distress ranging in total from 12 to 48, which we call the “general happiness” score [see, e.g., Taylor (2006) for the construction principles]. Low scores correspond to low levels of stress/depression (i.e., high feelings of well-being). This approach is known as a Likert scale. Although our main focus is on the last question, which we call the “overall happiness,” we also perform our analysis using the general happiness score for robustness purposes.

Appendix 2: Details of the Econometric Framework

The linear-in-means equation that we estimate is:

$$\begin{aligned} \omega _{i_g} = \beta _0 + \beta _1 \varvec{X}_{i_g} + \beta _2 \varvec{Y}_g + J m_g + \epsilon _{i_g}, \end{aligned}$$

where the variables are as they are described in Sect. 4.2. Taking the conditional mathematical expectations in both sides of the Eq. (8.1) yields

$$\begin{aligned} m_g = \beta _0 + \beta _1 \varvec{X}_{g} + \beta _2 \varvec{Y}_g + J m_g, \end{aligned}$$

where \(\varvec{X}_{g} = {\mathbb {E}}[\varvec{X}_{i_g}|g]\). The distinction between \(\varvec{X}_{g}\) and \(\varvec{Y}_{g}\) is key to understanding the identification problem. Any variable in \(\varvec{Y}_{g}\) has to describe something “meaningfully” related to that group; such as, the fraction of males, the mean education, the fraction of non-whites, etc. \(\varvec{X}_{g}\) is the group-level mean of individual-level observed characteristics and it may or may not coincide with \(\varvec{Y}_g\), i.e., not every variable in \(\varvec{X}_{g}\) has to correspond to an element in \(\varvec{Y}_{g}\).

In Eq. (8.2), \(m_g\) appears on both sides. Solving for \(m_g\) yields the expression that

$$\begin{aligned} m_g = \frac{\beta _0}{1 - J} + \frac{\beta _1}{1 - J} \varvec{X}_{g} + \frac{\beta _2}{1 - J} \varvec{Y}_g. \end{aligned}$$

Manski (1993) defines the reflection problem as follows. Let \(dim(\varvec{a})\) denote the dimension of some generic vector \(\varvec{a}\). The reflection problem states that if \(dim(\varvec{X}_{g}) = dim(\varvec{Y}_g)\), then linearity masks the econometric identification of the (endogenous) social interactions parameter \(J\) and the vector of contextual effect parameters \(\beta _2\). To see this clearly, one can plug Eq. (8.3) into Eq. (8.1) to obtain the outcome equation

$$\begin{aligned} \omega _{i_g} = \frac{\beta _0}{1 - J} + \beta _1 \varvec{X}_{i_g} + \frac{J \beta _1}{1 - J} \varvec{X}_{g} + \frac{\beta _2}{1 - J} \varvec{Y}_g + \epsilon _{i_g}. \end{aligned}$$

When the reflection problem is in effect, i.e., when \(dim(\varvec{X}_{g}) = dim(\varvec{Y}_g)\), \(J\) and \(\beta _2\) cannot be distinguished from each other econometrically, which implies that social interactions cannot be identified. For expositional purposes, we abuse the notation and set \(\varvec{X}_{g} = \varvec{Y}_g\), which yields the equation

$$\begin{aligned} \omega _{i_g} = \frac{\beta _0}{1 - J} + \beta _1 \varvec{X}_{i_g} + \frac{J\beta _1 + \beta _2}{1 - J} \varvec{Y}_g + \epsilon _{i_g}. \end{aligned}$$

Obviously, it is impossible to separate \(J\) from \(\beta _2\). One solution is to propose an additional \(X_g\) which is not in \(\varvec{Y}_g\); that is, we need to have \(dim(\varvec{X}_{g}) = dim(\varvec{Y}_g) + 1\). If such a \(X_g\) exists, then all the parameters in our linear-in-means model are identified. In other words, one individual-level variable, the mean of which cannot be regarded as a group-level variable, is required for identification of social interactions. To demonstrate this, let \(\tilde{X}_g\) be an element of the vector \(\varvec{X}_{g}\) and let \(\tilde{\beta }_1\) be the coefficient associated with \(\tilde{X}_g\). Let \(\tilde{X}_g \notin \varvec{Y}_g\) and \(dim(\varvec{X}_{g}) = dim(\varvec{Y}_g) + 1\). In other words, we let \(\tilde{X}_g\) define that additional variable in \(\varvec{X}_{g}\), which does not correspond to a contextual variable. Then, Eq. (8.4) can be rewritten as

$$\begin{aligned} \omega _{i_g} = \frac{\beta _0}{1 - J} + \beta _1 \varvec{X}_{i_g} + \frac{J \tilde{\beta }_1}{1 - J} \tilde{X}_{g} + \frac{J\bar{\beta }_1 + \beta _2}{1 - J} \varvec{Y}_g + \epsilon _{i_g}, \end{aligned}$$

where \(\bar{\beta }_1\) consists of the elements of \(\beta _1\) except \(\tilde{\beta }_1\). In other words, \(\beta _1 = [\tilde{\beta }_1 \bar{\beta }_1]\). From Eq. (8.6), \(\tilde{\beta }_1\) can be identified, which implies that \(J\) and \(\beta _2\) can separately be identified within this framework. Again, the key point is the existence of a variable \(\tilde{X}_g\), which does not correspond to a contextual variable \(\varvec{Y}_g\). Individual-level variables such as gender, education, age, marital status, and so on necessarily correspond to contextual effects when averaged out. One should find an individual-level variable \(\tilde{X}_g\) such that it cannot be interpreted as a group-level characteristic. If such a variable exists, it serves as an instrument and secures identification of \(J\) and \(\beta _2\) separately.

Appendix 3: Details of the Instrumental Variable

In this part, we explain the main intuition behind our IV strategy with an example. Suppose that there are many individuals in the population and each individual interacts with only a group of them. Individuals self-report the following happiness levels (everything is observed—the scale of the numbers is arbitrary, but the ordering of the days is consistent with the estimates reported in Table 2):

$$\begin{aligned}&M: (7+\epsilon _{M}) \\&T: (6+\epsilon _{T}) \\&W: (6+\epsilon _{W}) \\&T: (6+\epsilon _{T}) \\&F: (5+\epsilon _{F}) \\&St: (5+\epsilon _{St}) \\&S: (7+\epsilon _{S}). \end{aligned}$$

A lower score corresponds to a higher happiness level, just as in the BHPS dataset. Each individual’s type is given by the vector \(\varvec{\epsilon }=\bigl (\epsilon _{M}, \epsilon _{T}, \epsilon _{W}, \epsilon _{T}, \epsilon _{F}, \epsilon _{St}, \epsilon _{S}\bigr )\). We assume that \(\varvec{\epsilon }\) is independent and identically distributed (iid) across individuals. Elements of \(\varvec{\epsilon }\) have all zero means, but different variances. Thus, the joint distribution of \(\varvec{\epsilon }\) has mean 0 and a covariance matrix \(\varvec{\Sigma }\). Individuals are identical apart from their \(\varvec{\epsilon }\)’s. As a result, on average, we will have a 7, 6, 6, 6, 5, 5, 7 pattern from Monday to Sunday, just as the ordering of our estimates suggest. The overall mean is 6. However, this is the case at the population level. The group-level means would be different depending on the configuration of the \(\varvec{\epsilon }\)’s.

To resolve the reflection problem, we need one variable that is correlated with the individual-level happiness score, but does not correspond to a contextual effect. The mean of this variable should affect the individual’s happiness level only through the group-level happiness. Let’s say we put only “Saturday” as an instrument. The mean of this variable could in fact correspond to a contextual effect, because we would be picking those who are more likely to feel happier due to a possible self-selection into a Saturday interview.

We first make the observation that groups with a higher fraction of individuals interviewed on, say, a Wednesday or Friday or Sunday have higher average happiness levels. To capture this effect, we construct a dummy variable \(D=1\) if the individual is interviewed on either of these days, and 0 otherwise. Clearly, the group-level mean of \(D\) is not a meaningful contextual variable. But the incidental correlation between \(\epsilon _W\), \(\epsilon _F\), and \(\epsilon _S\) at the group level makes the group-level mean of \(D\) a strong determinant of social interactions. So, the main identifying assumption is that groups with a larger fraction of individuals interviewed on Wednesdays, Fridays, or Sundays have higher happiness levels on average. The empirical justification is presented in Table 7.

Table 7 \(p\)—values of the day dummies

To sum up, we try to obtain a “synthetic” day-of-the-week variable that can potentially resolve the reflection problem.

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Tumen, S., Zeydanli, T. Is Happiness Contagious? Separating Spillover Externalities from the Group-Level Social Context. J Happiness Stud 16, 719–744 (2015).

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