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Money and Happiness: Income, Wealth and Subjective Well-Being


We examine the complex relationship between money and happiness. We find that both permanent income and wealth are better predictors of life satisfaction than current income and wealth. They matter not only in absolute terms but also in comparative terms. However, their relative impacts differ. The first exerts a comparison effect—the higher the permanent income of the reference group, the lower life satisfaction—the second exerts an information effect—the higher the permanent wealth of the reference group, the higher life satisfaction. We also show that negative transitory shocks to income reduce life satisfaction while transitory shocks to wealth have no effect. Lastly, we analyse the effects of their components and find that not all of them predict life satisfaction: permanent taxes do not matter, while only the value of permanent real estate, financial and business assets do. Finally, we use quantile regression and analyse to what extent our results vary along the well-being distribution, finding the impacts to be larger at lower levels of life satisfaction.

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Fig. 1


  1. In an alternative specification, available upon request, we make use of a more standard equivalent income measure using the modified OECD scale instead of the per-capita measure. Regression results shown in the empirical section below are robust with respect to the choice of the two equivalence scales.

  2. By using linear models, we treat life satisfaction as cardinal. As life satisfaction is measured on an ordinal scale, ordered response models may be more appropriate. We re-estimated all our regressions using ordered logit model and confirm the findings of Ferrer-i-Carbonell and Frijters (2004): our conclusions do not depend upon the estimation method and results of the ordered logit models can be found in the online appendix.

  3. We also used these demographics separately to define the reference group and results remains qualitatively unchanged.

  4. Table A1 in our online appendix [] shows regression estimates that do not include the controls.

  5. One may worry that the difference in adjusted in R2 between columns (9) and (10) is too small to be statistically significant. To formally test whether the model with permanent and transitory income and wealth has a better predictive power than the model with actual income and wealth, we follow Wooldridge (2010) and perform a Vuong closeness test for non-nested models (Vuong 1989). The Vuong closeness test confirms that the predictive power of the former model is statistically higher (at the 1% level) than the latter model.

  6. To the extent measurement errors are non-permanent, our transitory shocks will reflect both true income shocks and measurement errors. Our results may thus be downward-biased; as we have more current income than wealth measurements, measurement noise in wealth could be greater relative to signal than for income, we might find transitory income matters but wealth not. It is, unfortunately, very difficult to think of a valid instrument – one that is uncorrelated with measurement errors and life satisfaction but is correlated with true transitory wealth shocks – that would allow us to address this.

  7. We replicated our analysis using other measures of rank (quartiles and deciles) and the results remain qualitatively the same.

  8. We also estimate the effects of gains and losses in the different components of income and wealth and find that only losses in capital income, in government income and in debt are significant predictors of future life satisfaction. Results (not reported for brevity) are available upon request.

  9. We performed a Vuong closeness test and confirm that the predictive power of the specification in the column (8) is significantly higher at the 1% level than the predictive power of the specification in the column (7).

  10. Using OLS without individual fixed effects raises the question of interpersonal comparability. But the systematic prediction of future outcomes by current subjective well-being scores using cross-section data show that they may be considered as interpersonally comparable (see Clark 2001 and Freeman 1978, for example, and De Neve et al. 2013, for a recent summary).

  11. We also explored heterogeneity with respect to sociodemographic characteristics such as age, education and level of income and wealth but failed to identify results significantly different. We also estimated separately our regressions for the wave “2007” and “2012” to account for potential differences due to the economic crisis of 2008 but results remain stable before and after the crisis.


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We are grateful to Andrew Clark for useful comments and thank the editor, Filomena Maggino, and two anonymous referees for very useful suggestions. D’Ambrosio and Lepinteur gratefully acknowledge financial support from the Fonds National de la Recherche Luxembourg. Some of the ideas of this paper were discussed by D’Ambrosio and Jäntti with Joachim R. Frick in 2008 during their time at DIW Berlin and appeared in D'Ambrosio et al. (2009). Joachim is still greatly missed. This work was funded by Fonds National de la Recherche Luxembourg (Grant Nos. FNR/P11/05 & FNR/P11/05BIS & FNR/P12/06).

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Correspondence to Conchita D’Ambrosio.

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D’Ambrosio, C., Jäntti, M. & Lepinteur, A. Money and Happiness: Income, Wealth and Subjective Well-Being. Soc Indic Res 148, 47–66 (2020).

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  • Permanent income
  • Permanent wealth
  • Life satisfaction
  • SOEP

JEL Classification

  • I30
  • D60