Social Indicators Research

, Volume 134, Issue 1, pp 359–384 | Cite as

Towards a Theory of Medium Term Life Satisfaction: Similar Results for Australia, Britain and Germany

  • Bruce HeadeyEmail author
  • Ruud Muffels


We analyse the Life Satisfaction trajectories of respondents in three long-running, national panel surveys: the Household, Income and Labour Dynamics Australia Survey (HILDA), the British Household Panel Survey (BHPS) and the German Socio-Economic Panel (SOEP). Previous research has shown that substantial minorities of respondents in all three countries recorded long term changes in LS (Fujita and Diener in J Personal Soc Psychol 88:158–64, 2005; Headey in Soc Indic Res 76:312–317, 2006; Headey et al. in Proc Natl Acad Sci 107:17922–7926, 2010; Headey et al. Soc Indic Res 112:725–48, 2013). In a recent SIR paper based on the German data (Headey and Muffels in Soc Indic Res, 2015. doi: 10.1007/s11205-015-1146-8), we showed graphs of LS trajectories which suggested—and subsequent statistical analysis confirmed—that respondents typically spend multiple consecutive years above and, in other periods, below their own long term mean level of LS. Here we extend the analysis to Australia and Britain, showing that results replicate in two more Western countries. It appears that most people go through relatively happy periods of life, and relatively unhappy periods. The evidence runs counter to set-point theory which views adult LS as stable, except for short term fluctuations due to life events. In the second half of the paper we try to contribute to a theory of medium term life satisfaction. We estimate structural equation models with two-way causation between LS and variables usually treated as causes of LS, including health, social support, frequency of social activities, and satisfaction with one’s work, partner and family life. In all three countries we find that there are positive feedback loops between these variables and LS, which partly account for extended periods of high or low LS. The two-way causation models are based on a modified concept of ‘Granger-causation’ (Granger in Econometrica 37(3):424–38, 1969). The main intuition behind Granger-causation is that if x can be shown to be statistically significantly related to y in a model which includes multiple lags of y, then it can be inferred that x is one cause of y.


Trajectories of life satisfaction Set-point theory Two-way causation Granger-causation Positive feedback loops Medium term change 



We would like to thank Ulrich Schimmack of the University of Toronto at Mississauga and Derek Headey of the International Food Policy Research Institute (IFPRI) for helpful suggestions on modelling reciprocal causation.


  1. Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being. New York: Plenum.CrossRefGoogle Scholar
  2. Argyle, M. (2001). The pyschology of happiness. London: Routledge.Google Scholar
  3. Australian Bureau of Statistics (ABS). (1996). National health and nutrition survey, 1995. Canberra: ABS.Google Scholar
  4. Beck, N., & Katz, J. N. (2011). Modelling dynamics in time-series cross-section political economy data. Annual Review of Political Science, 14, 331–352.CrossRefGoogle Scholar
  5. Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246.CrossRefGoogle Scholar
  6. Bentler, P. M., & Freeman, E. H. (1983). Tests for stability in linear structural equation systems. Psychometrika, 48, 143–45.CrossRefGoogle Scholar
  7. Brickman, P. D., & Campbell, D. T. (1971). Hedonic relativism and planning the good society. In M. H. Appley (Ed.), Adaptation level theory (pp. 287–302). New York: Academic Press.Google Scholar
  8. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit’. In K. A. Bollen & J. S. Long (Eds.), ‘Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.Google Scholar
  9. Costa, P. T., & McCrae, R. R. (1991). NEO PI-R. Odessa, FL: PAR.Google Scholar
  10. Deeg, D., & van R Zonneveld, (1989). Does happiness lengthen life? In R. Veenhoven (Ed.), How harmful is happiness? chap. 5. Rotterdam: Erasmus University Press.Google Scholar
  11. Diener, E. (1984). Subjective Well-Being. Psychological Bulletin, 95, 542–575.CrossRefGoogle Scholar
  12. Diener, E., & Seligman, M. E. P. (2004). Beyond money: Toward an economy of well-being. Psychological Science in the Public Interest, 5, 1–31.CrossRefGoogle Scholar
  13. Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 25, 276–302.CrossRefGoogle Scholar
  14. Dunn, E. W., Aknin, L. B., & Norton, M. I. (2008). Spending money on others promotes happiness. Science, 319, 1687–1688.CrossRefGoogle Scholar
  15. Easterlin, R. A. (2003). Explaining happiness. Proceedings of the National Academy of Sciences, 100(19), 11176–11183. doi: 10.1073/pnas.1633144100.CrossRefGoogle Scholar
  16. Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  17. Frey, B. S., & Stutzer, A. (2002). What can economists learn from happiness research? Journal of Economic Literature, 40, 402–435.CrossRefGoogle Scholar
  18. Frick, J. R., Schupp, J., & Wagner, G. G. (2007). Enhancing the power of the German socio-economic panel study (SOEP)—evolution, scope and enhancements. Schmollers Jahrbuch, 127, 139–169.Google Scholar
  19. Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2004). Money does matter! Evidence from increasing real income and life satisfaction in East Germany following reunification. American Economic Review, 94, 730–740.CrossRefGoogle Scholar
  20. Fujita, F., & Diener, E. (2005). Life satisfaction set-point: Stability and change. Journal of Personality and Social Psychology, 88, 158–164.CrossRefGoogle Scholar
  21. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.CrossRefGoogle Scholar
  22. Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120.CrossRefGoogle Scholar
  23. Gremeaux, V., Gayda, M., Lepers, R., Sosner, P., Juneau, M., & Nigam, A. (2012). Exercise and longevity. Maturitas, 73, 312–317.CrossRefGoogle Scholar
  24. Headey, B. W. (2006). Subjective well-being: revisions to dynamic equilibrium theory using national panel data and panel regression methods. Social Indicators Research, 79, 369–403.CrossRefGoogle Scholar
  25. Headey, B. W. (2008). Life goals matter to happiness: A revision of set-point theory. Social Indicators Research, 86, 213–231.CrossRefGoogle Scholar
  26. Headey, B. W., Hoehne, G., & Wagner, G. G. (2014). Does religion make you healthier and longer lived? Evidence for Germany. Social Indicators Research, 119, 1335–1361.CrossRefGoogle Scholar
  27. Headey, B. W., & Muffels, R. J. A. (2015). Towards a theory of medium term life satisfaction: Two-way causation partly explains persistent satisfaction or dissatisfaction. Social Indicators Research, 124, 2. doi: 10.1007/s11205-015-1146-8.Google Scholar
  28. Headey, B. W., Muffels, R. J. A., & Wagner, G. G. (2010a). Long-running German panel survey shows that personal and economic choices, not just genes, matter for happiness. Proceedings of the National Academy of Sciences, 107(42), 17922–17926.CrossRefGoogle Scholar
  29. Headey, B. W., Muffels, R. J. A., & Wagner, G. G. (2013). Choices which change life satisfaction: Similar results for Australia, Britain and Germany. Social Indicators Research, 112, 725–748.CrossRefGoogle Scholar
  30. Headey, B. W., Schupp, J., Tucci, I., & Wagner, G. G. (2010b). Authentic happiness theory supported by impact of religion on life satisfaction: A longitudinal analysis with data for Germany. Journal of Positive Psychology, 5, 73–82.CrossRefGoogle Scholar
  31. Headey, B. W., Veenhoven, R., & Wearing, A. J. (1991). Top-down versus bottom-up theories of subjective well-being. Social Indicators Research, 24, 81–100.CrossRefGoogle Scholar
  32. Henderson, S., Byrne, D. G., & Duncan-Jones, P. (1981). Neurosis and the social environment. New York: Academic Press.Google Scholar
  33. Kessler, R. C., & Greenberg, D. F. (1981). Linear panel analysis. New York: Academic Press.Google Scholar
  34. Kuskova, V. A. (2011). A longitudinal analysis of the relationship between life satisfaction and employee volunteerism. Academy of Management Proceedings, 1, 1–6.CrossRefGoogle Scholar
  35. Lance, C. E., Mallard, A. G., & Michalos, A. C. (1995). Tests of the causal directions of global-life facet satisfaction relationships. Social Indicators Research, 34, 69–92.CrossRefGoogle Scholar
  36. Lucas, R. E. (2008). Personality and subjective well-being. In M. Eid & R. J. Larsen (Eds.), The science of subjective well-being (pp. 171–194). New York: Guilford Press.Google Scholar
  37. Lucas, R. E., Clark, A. E., Georgellis, Y., & Diener, E. (2003). Reexamining adaptation and the set point model of happiness: Reactions to change in marital status. Journal of Personality and Social Psychology, 84, 527–539.CrossRefGoogle Scholar
  38. Luhmann, M., & Eid, M. (2009). Does it really feel the same? Changes in life satisfaction following repeated life events. Journal of Personality and Social Psychology, 97, 363–381.CrossRefGoogle Scholar
  39. Luhmann, M., Hoffman, W., Eid, M., & Lucas, R. E. (2012). Subjective well-being and adaptation to life events: A meta-analysis. Journal of Personality and Social Psychology, 102, 592–615.CrossRefGoogle Scholar
  40. Lykken, D., & Tellegen, A. (1996). Happiness is a stochastic phenomenon. Psychological Science, 7, 186–189.CrossRefGoogle Scholar
  41. Lynn, P. (2006). Quality profile: BHPS version 2.0: waves 1 to 13 1991–2003. Colchester: Institute for Social Research, University of Essex.Google Scholar
  42. Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9, 111–131.CrossRefGoogle Scholar
  43. Mathison, L., Andersen, M. H., Veenstra, M., Wahl, A. K., Hanestad, B. R., & Fosse, E. (2007). Quality of life can both influence and be an outcome of general health perceptions after heart surgery. Health and Quality of Life Outcomes, 5, 27. doi: 10.1186/1477-7525-5-27.CrossRefGoogle Scholar
  44. Mehnert, T., Kraus, H. H., Nadler, R., & Boyd, M. (1990). Correlates of life satisfaction in those with a disabling condition. Rehabilitation Psychology, 35, 3–17.CrossRefGoogle Scholar
  45. Meier, S., and Stutzer, A. (2004). Is volunteering rewarding in itself? IZA Discussion Paper No 1045 (March) IZA, Bonn.Google Scholar
  46. Nagazato, N., Schimmack, U., & Oishi, S. (2011). Effect of changes in living conditions on well-being: A prospective top-down bottom-up model. Social Indicators Research, 100, 115–135.CrossRefGoogle Scholar
  47. Nickerson, C., Schwarz, N., Diener, E., & Kahneman, D. (2003). Zeroing in on the dark side of the American dream: A closer look at the negative consequences of the goal for financial success. Psychological Science, 14, 531–536.CrossRefGoogle Scholar
  48. Pearl, J. (2009). Causality: Models, reasoning and inference. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  49. Scherpenzeel, A., & Saris, W. E. (1996). Causal direction in a model of life satisfaction: the top-down/bottom-up controversy. Social Indicators Research, 38, 161–180.CrossRefGoogle Scholar
  50. Schwarze, J., Andersen, H., and Silke, A. (2000). Self-rated health and changes in self-rated health as predictors of mortality—first evidence from the German panel data, DIW Discussion Paper No. 203, Berlin, DIW.Google Scholar
  51. Sheldon, K. M., & Lucas, R. E. (2014). The stability of happiness. Amsterdam: Elsevier.Google Scholar
  52. StataCorp. (2013). Structural equation modelling reference manual, release 13. College Station, TX: Stata Press.Google Scholar
  53. Thoits, P. A., & Hewitt, L. N. (2001). Volunteer work and well-being. Journal of Health and Social Behavior, 42, 115–131.CrossRefGoogle Scholar
  54. Ware, J. E., Snow, K., & Kosinski, M. (2000). SF-36 health survey: Manual and interpretation guide. Lincoln, RI: QualityMetric Inc.Google Scholar
  55. Watson, N., & Wooden, M. (2004). Assessing the quality of the HILDA survey wave 2 data. Melbourne: Melbourne Institute of Applied Economic and Social Research.Google Scholar
  56. Wilkins, A. S. (2014). To lag or not to lag: Re-evaluating the use of lagged dependent variables in regression analysis. Working Paper, Stanford University Department of Political Science. Downloaded July 4, 2014.Google Scholar
  57. Wooldridge, J. M. (2010). Econometric analysis of cross-section and panel data (2nd ed.). Cambridge, Mass: MIT Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Melbourne Institute of AppliedParkvilleAustralia

Personalised recommendations