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Well-Being and Ill-Being: A Bivariate Panel Data Analysis

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  • Wang-Sheng Lee

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Umut Oguzoglu

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

Abstract

The aim of this paper is to estimate in a multivariate context the factors associated with well-being and ill-being without making the assumptions that they are opposite ends of the same continuum, and that the factors uniformly affect both well-being and ill-being. Using the first five waves of panel data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, we jointly model positive and negative well-being in a two-equation dynamic panel data model. We found that while past ill-being had significant effect on current well-being there was no support for a reverse relationship (i.e. lagged effect of well-being on current ill-being). In addition, we also found support for asymmetry in how certain factors affect well-being and ill-being. The implication of the findings in this paper for the happiness literature is that for future empirical work, it would perhaps more prudent to begin with the notion that well-being and ill-being are distinct dimensions, that the unobservables that affect well-being and ill-being are correlated, and to specify econometric models that allow for these concepts to be reflected.

Suggested Citation

  • Wang-Sheng Lee & Umut Oguzoglu, 2007. "Well-Being and Ill-Being: A Bivariate Panel Data Analysis," Melbourne Institute Working Paper Series wp2007n28, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2007n28
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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2007n28.pdf
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    References listed on IDEAS

    as
    1. Bruce Headey & Mark Wooden, 2004. "The Effects of Wealth and Income on Subjective Well‐Being and Ill‐Being," The Economic Record, The Economic Society of Australia, vol. 80(s1), pages 24-33, September.
    2. Rob Alessie & Stefan Hochguertel & Arthur van Soest, 2004. "Ownership of Stocks and Mutual Funds: A Panel Data Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 783-796, August.
    3. Stefan Boes & Rainer Winkelmann, 2006. "The Effect of Income on Positive and Negative Subjective Well-Being," SOI - Working Papers 0605, Socioeconomic Institute - University of Zurich.
    4. repec:bla:ausecr:v:37:y:2004:i:3:p:343-349 is not listed on IDEAS
    5. Rainer Winkelmann, 2005. "Subjective well-being and the family: Results from an ordered probit model with multiple random effects," Empirical Economics, Springer, vol. 30(3), pages 749-761, October.
    6. repec:bla:ausecr:v:40:y:2007:i:4:p:369-384 is not listed on IDEAS
    7. Bruce Headey & Jonathan Kelley & Alex Wearing, 1993. "Dimensions of mental health: Life satisfaction, positive affect, anxiety and depression," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 29(1), pages 63-82, May.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    9. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
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    Cited by:

    1. Imen Karaa, 2018. "Moral Hazard and Learning in the Tunisian Automobile Insurance Market: New Evidence from Dynamic Data," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 43(3), pages 560-589, July.

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    More about this item

    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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