From Average Joe's happiness to Miserable Jane and Cheerful John: using quantile regressions to analyze the full subjective well-being distribution

Standard regression techniques are only able to give an incomplete picture of the relationship between subjective well-being and its determinants since the very idea of conventional estimators such as OLS is the averaging out over the whole distribution: studies based on such regression techniques thus are implicitly only interested in Average Joe's happiness. Using cross-sectional data from the British Household Panel Survey (BHPS) for the year 2006, we apply quantile regressions to analyze effects of a set of explanatory variables on different quantiles of the happiness distribution and compare these results with a standard regression. Among our results we observe a decreasing importance of income, health status and social factors with increasing quantiles of happiness. Another finding is that education has a positive association with happiness at the lower quantiles but a negative association at the upper quantiles. We explore the robustness of our findings in various ways.

Research highlights
► Standard regressions give an incomplete picture since the very idea of conventional estimators is the averaging out over the whole distribution.
► We apply quantile regressions to analyze the different quantiles of the happiness distribution for data from the BHPS for the year 2006.
► The results are compared with a standard regression.
► Among our results we observe a decreasing importance of income, health status and social factors with increasing quantiles of happiness.
► Another finding is that education has a positive association with happiness at the lower quantiles but a negative association at the upper quantiles.