Modelling the Latent Components of Personal Happiness
We discuss a class of statistical models able to measure the self-evaluation of happiness by means of a sample of respondents and investigate the ability of this proposal to enhance the different contribution of subjective, environmental and economic variables. The approach is based on a mixture model introduced for interpreting the ordered level of happiness as a combination of a real belief and a surrounding uncertainty: these unobserved components may be easily parameterized and immediately related to subjects’ covariates. An empirical evidence is supported on data set derived by the Survey of Household Income and Wealth (SHIW) conducted by the Bank of Italy.
KeywordsHappiness Ordinal data cub models SHIW data set
This work has been partly supported by projects FIRB 2012 “Mixture and latent variable model for causal inference and analysis of socio-economic data” (code RBFR12SHVV), University of Perugia, and STAR 2013, University of Naples Federico II.
- 5.Iannario, M., Piccolo, D.: cub models: statistical methods and empirical evidence. In: Kenett, R.S., Salini, S. (eds.) Modern Analysis of Customer Surveys: With Applications Using R, pp. 231–258. Wiley, Chichester (2012) Google Scholar
- 6.Piccolo, D.: On the moments of a mixture of uniform and shifted binomial random variables. Quad. Stat. 5, 85–104 (2003) Google Scholar
- 7.Stiglitz, J.E., Sen, A., Fitoussi, J.P.: Report by the Commission on the Measurement of Economic Performance and Social Progress (2009) Google Scholar