This paper presents evidence for the existence of 'set-points' for subjective wellbeing. Our results derive from a 10-year longitudinal study in which subjective wellbeing has been measured using a single question of general life satisfaction. The process of data analysis is driven by logic based on the theory of subjective wellbeing homeostasis. This analysis involves the iterative elimination of raw data, from 7,356 individual respondents, based on confidence limits. All results are projected onto a 0 -100 point scale. We demonstrate evidence for the existence of set-points lying between 71 -90 points, with an average setpoint-range of 18 -20 points for each person. The implications and limitations of these findings are discussed.

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In social and economic analysis of longitudinal data, the socioeconomic variables that are statistically significant in pooled data regressions sometimes become insignificant after individual fixed effects are controlled for. This phenomenon has been observed in the analysis of the relationship between age and happiness. The discrepancy in results between regressions with and without controlling for individual fixed effects is sometimes known as a mystery in the research of age and happiness. This paper points out that cross-sectional information and longitudinal information reflect distinct aspects of the phenomenon under study. In age-happiness studies, cross-sectional information describes whether, in a particular year, people of a certain age are happier than people of other ages. Longitudinal information describes whether people become happier or less happy over the life cycle. The former compares happiness between different people, and the later compares happiness within the same person. Average happiness is U-shaped in age among different cohorts, and simultaneously decreases with age in the life cycle within individuals. Using data on happiness from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, this paper explains what "individual fixed effects are controlled for" means in the context of FE regression, gives insight into the age-happiness puzzle and raises awareness of the multidimensionality of longitudinal data.

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