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Dynamic Analysis and the Economics of Happiness: Rationale, Results and Rules

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  • Piper, Alan T.

Abstract

This paper provides a sustained introduction for the use of dynamic panel methods when analysing life satisfaction. As well as being able to address the issue of serial correlation, dynamic panel analysis also has the advantage of being able to treat variables as exogenous or endogenous, important for happiness, and can generate both contemporaneous and long run estimates for independent variables. A key result found initially for young people, but which is robust to different age ranges and countries, is that happiness is largely contemporaneous although there is a small, persistent effect of the past on current happiness. Additionally, decision rules are provided for the analysis of happiness using dynamic panel analysis.

Suggested Citation

  • Piper, Alan T., 2012. "Dynamic Analysis and the Economics of Happiness: Rationale, Results and Rules," MPRA Paper 43248, University Library of Munich, Germany, revised Dec 2012.
    • Handle: RePEc:pra:mprapa:43248
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    References listed on IDEAS

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    21. Nattavudh Powdthavee, 2009. "Estimating the Causal Effects of Income on Happiness," Discussion Papers 09/02, Department of Economics, University of York.
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    Citations

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    Cited by:

    1. Alan Piper, 2015. "Heaven knows I'm miserable now: overeducation and reduced life satisfaction," Education Economics, Taylor & Francis Journals, vol. 23(6), pages 677-692, December.
    2. Alan Piper, 2015. "Europe’s Capital Cities and the Happiness Penalty: An Investigation Using the European Social Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 123(1), pages 103-126, August.
    3. Piper, Alan T., 2013. "Happiness, Dynamics and Adaptation," MPRA Paper 52342, University Library of Munich, Germany.
    4. Paweł Chrostek, 2016. "An Empirical Investigation into the Determinants and Persistence of Happiness and Life Evaluation," Journal of Happiness Studies, Springer, vol. 17(1), pages 413-430, February.
    5. Piper, Alan, 2013. "A Note on Modelling Dynamics in Happiness Estimations," MPRA Paper 49364, University Library of Munich, Germany.
    6. Piper, Alan T., 2014. "The Benefits, Challenges and Insights of a Dynamic Panel assessment of Life Satisfaction," MPRA Paper 59556, University Library of Munich, Germany.
    7. Piper, Alan T., 2018. "Adult life satisfaction: largely (though not wholly) contemporaneous? A System General Method of Moments dynamic panel analysis," MPRA Paper 85601, University Library of Munich, Germany.
    8. Chrostek, Pawel, 2013. "An empirical investigation into the determinants and persistence of happiness and life evaluation," MPRA Paper 50442, University Library of Munich, Germany.
    9. Alan Piper, 2023. "What Does Dynamic Panel Analysis Tell Us About Life Satisfaction?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 376-394, June.
    10. Chrostek, Pawel, 2013. "An empirical investigation into the determinants and persistence of different types of subjective well-being," MPRA Paper 48292, University Library of Munich, Germany.
    11. Paweł Chrostek, 2016. "An Empirical Investigation into the Determinants and Persistence of Happiness and Life Evaluation," Journal of Happiness Studies, Springer, vol. 17(1), pages 413-430, February.
    12. Piper, Alan T., 2014. "An Investigation into Happiness, Dynamics and Adaptation," MPRA Paper 57778, University Library of Munich, Germany.
    13. Alan Piper, 2018. "Adult life satisfaction largely (though not wholly) contemporaneous," Discussion Papers 028, Europa-Universität Flensburg, International Institute of Management.

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    More about this item

    Keywords

    Dynamic Panel Analysis; Happiness; Life Satisfaction;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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