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How Interdependent are Cross-Country Happiness Dynamics?

Social Indicators Research volume 122, pages 491–518 (2015)Cite this article

Abstract

We characterize evolution of cross-country happiness dynamics by two important factors. The first one concerns inertia, which we model in a non-linear and stochastic environment to reflect on how an agent’s own past level of happiness adapts to the current level. Second, we allow an explicit role of socio-economic ‘distance’ in facilitating dynamic interdependence among happiness levels. A series of hypotheses are tested on a long-time series data for a sample of twelve European countries. We find that inertia has a strong positive and non-linear effect on countries’ steady-state happiness convergence. The effects are more pronounced when relative income and macroeconomic variables are allowed to determine the correlation structure of happiness. The main novelty is the consideration of spatio-temporal dynamic aspects of happiness where complementarity in the latter across economies is rigorously tested and established. We find that after accounting for the effects of heterogenous socio-economic dynamics, cross-country happiness is indeed complementary: a unit rise in happiness level in one country raises happiness in others. Our analytical model and empirical findings have interesting socio-economic policy implications.

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Notes

  1. Throughout the paper the terms ‘life-satisfaction’ and ‘happiness’ will be used interchangeably to represent subjective well-being. The analysis presented in this paper concerns country level happiness/life-satisfaction dynamics.

  2. We thank an anonymous referee for pointing out the relevance of this aspect to our research.

  3. The growth of the happiness indicates temporal dependence on past observations. This can be characterized by both inertia and by the past values of other determinants. Binder and Coad (2010), Bottan and Truglia (2011), and D’Ambrosio and Frick (2012) study different dimensions of inertia and their interdependence dynamics.

  4. A series of influential contributions from Easterlin et al. in the comparison of happiness-income profile across economies and over time provided the much needed motivation for the current work.

  5. Our sincere thanks go to Richard Easterlin and Laura Angelescu who have kindly provided their data set, which lent to the comparison of our own constructed data.

  6. For cross-country panel unit root test we can employ Levin-Lin-Chu (LLC) method where it is assumed that \(\rho\) is the same across all countries: \(\Delta h_{it} = \alpha _i + \rho h_{i,t-1} + \sum _{k=1}^{n} \Delta h_{i,t-k} + \delta _i t + u_{it}\). Here \(\Delta\) is the first difference of \(h_{it}\); \(\delta\) is the coefficient of time trend. In contrast, one can allow \(\rho\) to differ for each \(i\) and accommodate heterogeneity in the form of individual unit root process following Im-Pesaran-Schmidt (IPS) method. In this case, \(H_0: \rho _i\) = 0 for all \(i\) and \(H_1: \rho _i\) \(<\) 0 for at least one \(i\). Under \(H_0\) all happiness series are assumed to be non-stationary processes whereas under alternative hypothesis it implies that at least a fraction of the series in the panel are stationary.

  7. See, Chen and Conley (2001) for detailed description on the conditional mean and variance of spatial VAR.

  8. We sincerely thank Richard Easterlin and Laura Angelescu for kindly sharing their data set for the present study.

  9. While it is possible that the spatial dynamics results might experience marginal change, other inferences—which are based on individual country characteristics would remain unchanged.

  10. Many thanks to the anonymous referee for underlining the importance of the same while interpreting our results.

  11. For robustness check we have also collected data from the World Happiness Database and have re-estimated our models. The results—despite some minor change in values—do not alter the conclusions of the paper.

  12. This division also roughly corresponds to one of the referee’s concerns that the literature proves that there is a strong happiness increases around the age of 60 followed by a major decline after 75’.

  13. Many thanks to a referee who pointed out an error.

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Authors and Affiliations

  1. Department of Accounting, Finance and Economics, Swansea University, Singleton Park, Swansea, SA2 8PP, UK

    Tapas Mishra & Mamata Parhi

  2. Departamento de Industrias, Economía and Negocios, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaiso, Chile

    Raúl Fuentes

Authors
  1. Tapas Mishra
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  2. Mamata Parhi
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  3. Raúl Fuentes
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Correspondence to Tapas Mishra.

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Mishra, T., Parhi, M. & Fuentes, R. How Interdependent are Cross-Country Happiness Dynamics?. Soc Indic Res 122, 491–518 (2015). https://doi.org/10.1007/s11205-014-0692-9

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Keywords

  • Inertia
  • Happiness persistence
  • Relative income
  • Convergence
  • Spatial dynamics