Making inference of British household's happiness efficiency: A Bayesian latent model

In this paper, we propose a novel approach whereby happiness for British households is identified within a latent model frontier analysis using longitudinal data. By doing so we overcome issues related to the measurement of happiness. To estimate happiness frontier and thereby happiness efficiency, we employ a Bayesian inference procedure organized around Sequential Monte Carlo (SMC) particle filtering techniques. In addition, we propose to consider individual-specific characteristics by estimating happiness efficiency models with individual-specific thresholds to happiness. This is the first study that treats happiness as a latent variable and departs from restrictions that happiness efficiency would be time invariant. Our results show that happiness efficiency is related to the welfare loss associated with potentially misusing the resources that British individuals have at their disposal. Key to happiness is to have certain personality traits, such as being agreeable and extravert as they assist efforts to enhance happiness efficiency. On the other hand, being neurotic impairs happiness efficiency.