Handbook of Research Methods and Applications in Happiness and Quality of Life
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Handbook of Research Methods and Applications in Happiness and Quality of Life

Edited by Luigino Bruni and Pier Luigi Porta

Offering a thorough assessment of the recent developments in the economic literature on happiness and quality of life, this Handbook astutely considers both methods of estimation and policy application. The expert contributors critically present in-depth research on a wide range of topics including culture and media, inequality, and the relational and emotional side of human life. Accessible and far-reaching, it will prove an invaluable resource for students and scholars of welfare and economics.
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Chapter 17: Happiness and health

Luca Crivelli, Sara Della Bella and Mario Lucchini


Studies concerning the determinants of subjective well-being (SWB), conducted in several countries and based on different datasets and methods, have all shown that health is one of the strongest predictors of individual happiness. However, more work is necessary in order to determine whether this relationship is a truly causal one and to unravel the temporal dynamics of the effect of health on SWB. In this chapter we aim, first of all, to provide an accurate estimate of the effect of self-assessed health on SWB by using the Swiss Household Panel dataset and panel data models that enable us to get rid of unobserved heterogeneity, which represents the main obstacle in models trying to estimate causal effects. As a second objective, we focus on the study of the temporal dynamics underlying the emergence of happiness and the relationship between happiness and health. We are interested in clarifying: (a) whether people are able to fully adjust to past health circumstances as well as to past life events and (b) whether SWB is autoregressive. In other terms, we investigated the existence of general and specific habituation in SWB. In this chapter we applied a FE model in order to investigate specific habituation channels and a GMM model in order to understand whether life satisfaction (our indicator of SWB) is autoregressive. In conclusion our models confirm the strong association between health and SWB revealed by previous studies. Both the FE and the GMM model prove that current health is a strong predictor of SWB.

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