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Going Beyond Average Joe's Happiness: Using Quantile Regressions to Analyze the Full Subjective Well-Being Distribution

  • Martin Binder

    ()

  • Alex Coad

    ()

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 an ordinary least squares regression. We also analyze some reversed relationships, where happiness enters the regression equation as an explanatory variable (e.g., the effects of happiness on individual's financial success). 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.

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Paper provided by Philipps University Marburg, Department of Geography in its series Papers on Economics and Evolution with number 2010-10.

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Date of creation: Jul 2010
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Publication status: Published as "From Average Joe's happiness to Miserable Jane and Cheerful John: using quantile regressions to analyze the full subjective well-being distribution", online first in: Journal of Economic Behavior & Organization, 2011, doi:10.1016/j.jebo.2011.02.005.
Handle: RePEc:esi:evopap:2010-10
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  1. Ada Ferrer-i-Carbonell & Paul Frijters, 2002. "How important is Methodology for the Estimates of the Determinants of Happiness?," Tinbergen Institute Discussion Papers 02-024/3, Tinbergen Institute.
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