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The Structure of Subjective Well-Being and Its Determinants: A Micro-Data Study for Portugal

Social Indicators Research volume 126, pages 1375–1399 (2016)Cite this article

Abstract

The Subjective Well-Being status has gained a growing research attention in social sciences during the last decades. The attention given by the academic world to this issue has been followed by the community in general. This line of research is still undeveloped in Portugal, and therefore needs further investigation. The purpose of this study is to analyze the determinants of Life Satisfaction and Happiness, two dimensions of Subjective Well-Being of the Portuguese citizens using micro-data from the European Quality of Life Survey. Ordinary Least Squares regressions and Ordinal Logistic models are estimated to identify the main factors that explain well-being in Portugal. We find that trust in public institutions, satisfaction with material conditions, volunteering activities and employment status have a positive and significant effect on Life Satisfaction. Our evidence also shows that satisfaction with family, satisfaction with material conditions, participation in sport activities, optimism and the marital status are relevant factors in explaining citizen’s Happiness in Portugal. The results are similar using Ordinary Least Squares or Maximum Likelihood estimation techniques on ordinal logistic models.

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Notes

  1. http://www.europeansocialsurvey.org/docs/methodology/ESS_weighting_data_1.pdf. Accessed on 8 December 2014.

  2. This method maximizes the ML function with respect to the coefficients β i and the thresholds \(\alpha_{1} , \alpha_{2} , \ldots , \alpha_{K - 1} ,\) points using an iterative procedure that finds the estimates which guarantee the maximum probability for the SWB i value to occur (more details can be found in Gujarati and Porter 2009).

  3. The test of parallel lines is given by \(X_{\textit{LP}}^{2} = - 2\textit{LL}_{{H_{A} }} - \left( { - 2 \textit{LL}_{{H_{0} }} } \right) = - 2Ln \left[ {\frac{{L_{{H_{A} }} }}{{L_{{H_{0} }} }}} \right]\sim X_{{\left( {K - 2} \right)p}}^{2}\). Decision rule implies that if \(X_{\textit{LP}}^{2} > X^{2}\) (or p value < 0.05) the null hypothesis (H 0 of parallel slops is rejected, i.e., the regression coefficients are different across all levels of response variables.

  4. The F statistic is given by\(F - \textit{stat} = ((\textit{RSS}_{r} - \textit{RSS}_{ur} )/n)/(\textit{RSS}_{ur} /(N - n - 1)) \sim F_{n,N - n - 1}\), where RSSr and RSSur are the residual sum of squares of the restricted and unrestricted model, respectively. Decision rule implies that if \(F -\textit{stat}. > F_{n, N - n - 1} \left( {1 - \alpha } \right)\) (or p value < 0.05) the null hypothesis (\(H_{0} :\beta_{1} = \beta_{2} = \cdots = \beta_{n} = 0)\) is rejected and at least one of the coefficients is different from zero (Gujarati and Porter 2009).

  5. The Likelihood Ratio is given by \(LR = 2\left( {\textit{LLF}_{ur} - LLF_{r} } \right)\sim X_{n}^{2}\) where \(\textit{LLF}_{ur}\) and LLF r are the log-likelihood values of the unrestricted and restricted model, respectively, and n the number of restrictions. Decision rule implies that if \(\textit{LR} > X^{2}\) (or p value < 0.05) the restricted model is rejected and at least one of the coefficients is different from zero (Gujarati and Porter 2009).

  6. The t statistic is given by \(t - \textit{stat} = \left( {\hat{\beta }_{i} - \beta_{i} } \right)/\sqrt {\text{var} \left( {\hat{\beta }_{i} } \right)\sim t_{N - n - 1} .}\) Decision rule implies that if \(\left| t - \textit{stat} \right| \ge t_{N - n - 1} (1 - \alpha /2)\) (or p value < 0.05) the null hypothesis \((H_{0} :\beta_{i} = 0)\) is rejected and the parameter is considered statistically significant at the conventional level (Gujarati and Porter 2009).

  7. The Wald statistic is given by \(\textit{Wald} = \left( {\hat{\beta }/S.E._{\beta } } \right)^{2} \sim X^{2}\). To test the null hypothesis we compare this result to a X 2 distribution with one degree of freedom. Decision rule implies that when the Wald Chi square statistic is greater than the critical value of the X 2 distribution (or p value < 0.05), we reject the null hypothesis (H 0:β 1 = 0)).

  8. We have also tested the convexity hypothesis of the impact of age by introducing the square of age variable in Life Satisfaction and Happiness equations. The regression results do not confirm the existence of a threshold point where Life Satisfaction and Happiness decrease when a cut-off point in age is reached.

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Acknowledgments

We are grateful to two anonymous referees for their useful comments and suggestions. We acknowledge the free access of data provided by the European Quality of Life Survey. UK Data Services at University of Essex, Colchester is the data archive. The European Foundation for the Improvement of Living and Working Conditions is simultaneously the depositor, the principal investigator and the sponsor (http://dx.doi.org/10.5255/UKDA-SN-7316-1).

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Authors and Affiliations

  1. Faculty of Economy, University of Coimbra, Coimbra, Portugal

    Elias Soukiazis & Sara Ramos

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  1. Elias Soukiazis
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Correspondence to Elias Soukiazis.

Appendix

Appendix

See Tables 6, 7, 8 and 9.

Table 6 Description of the variables
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Table 7 Descriptive statistics of the selected variables
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Table 8 Descriptive statistics of the selected variables (dummies)
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Table 9 Correlations between explanatory variables and SWB (life satisfaction and happiness)
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Soukiazis, E., Ramos, S. The Structure of Subjective Well-Being and Its Determinants: A Micro-Data Study for Portugal. Soc Indic Res 126, 1375–1399 (2016). https://doi.org/10.1007/s11205-015-0938-1

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Keywords

  • Factor analysis
  • Happiness
  • Life satisfaction
  • Ordinal logit models
  • Subjective Well-Being