No abstract

Our empirical analysis on the determinants of self declared happiness on more than 100,000 individuals from representative samples in 82 world countries does not reject the hypothesis that the time spent for relationships has a significant and positive impact on happiness. This basic nexus helps to understand new unexplored paths in the so called "happiness-income paradox". To illustrate them we show that personal income has two main effects on happiness. The first is a positive relative income effect which depends on individual's ranking within domestic income quantiles. The second is determined by the relationship between income and relational goods. In principle, more productive individuals may substitute (if the income effect prevails over the substitution effect) worked hours with the nonworking time made free for enjoying relationships, when they have strong preferences for them. The problem is that these individuals tend to have ties with their income class peers who share with them a high opportunity cost for the time spent for relationships. Hence, a coordination failure may reduce the joint investment in relational goods (local public goods which need to be co-produced in order to be enjoyed together) and, through this effect, individuals in the highest income quantiles may end up with poorer relational goods. The impact of personal income on happiness through this channel is therefore expected to be negative.

SUMMARYWe define a bivariate mixture model to test whether economic growth can be considered exogenous in the Solovian sense. For this purpose, the multivariate mixture approach proposed by Alfò and Trovato is applied to the Bernanke and Gürkaynak extension of the Solow model. We find that the explanatory power of the Solow growth model is enhanced, since growth rates are not statistically significantly associated with investment rates, when cross-country heterogeneity is considered. Moreover, no sign of convergence to a single equilibrium is found.

The analysis of overdispersed counts has been the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity or dependence among subjects. In this paper we extend the standard variance component models to the analysis of multivariate counts, defining the dependence among counts through a set of correlated random coefficients. Estimation is carried out by numerical integration through an EM algorithm without parametric assumptions upon the random coefficients distribution. The proposed model is computationally parsimonious and, when applied to a real dataset, seems to produce better results than parametric models. A simulation study has been carried out to investigate the behaviour of the proposed models in a series of empirical situation

The nexus between corporate social responsibility and corporate performance is of fundamental importance to understand if the former can be a sustainable strategy in the competitive race. In this paper we test this relationship on a sample of firms observed in a 13-year interval by focusing on a performance indicator (productive efficiency) seldom explored in this literature with a novel approach (latent class stochastic frontiers). Our empirical findings show that firms included in the Domini 400 index (a CSR stock market index) do not appear to be more distant from the production frontier than firms in the control sample after controlling for the heterogeneity of production structure.