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Retirement and the Marginal Utility of Income

Author

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  • Andrew E. Clark

    (PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Yarine Fawaz

    (UAB - Universitat Autònoma de Barcelona = Autonomous University of Barcelona = Universidad Autónoma de Barcelona)

Abstract

The individual level of subjective well-being (SWB) has been shown to predict a number of future observable outcomes. Behaviour may however also be affected by the slope of SWB with respect to certain variables. We here use latentclass analysis to model both intercept and slope heterogeneity in the SWB-income relationship, and construct a continuous measure of the marginal utility of income. We show this marginal utility does predict future behaviour: those who value income more (who have a higher income elasticity of well-being) are less likely to retire. This correlation is found conditional on both the level of income and the level of well-being.

Suggested Citation

  • Andrew E. Clark & Yarine Fawaz, 2015. "Retirement and the Marginal Utility of Income," PSE Working Papers halshs-01189009, HAL.
    • Handle: RePEc:hal:psewpa:halshs-01189009
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01189009
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    References listed on IDEAS

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    Keywords

    Subjective Well-being; Retirement; Marginal Utility of Income; Latent Class Models;
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