Behavioral economics has two ‘souls’: Do they both depart from economic rationality?

In this paper, I argue that behavioral economics, far from being a monolithic theory, consists of two different ‘souls’ and that a fundamental asymmetry exists between them, with regard to the nature of the departures from the economics’ ‘canonical model’ they focus on. While a first class of departures deals with the major cognitive limitations and systematic biases in decision-making affecting economic behavior, a second research area investigates deviations from the classic assumption that economic agents are systematically driven by the pursuit of material self-interest. Even though on methodological grounds the two research areas share a broadly inductive approach – as they both aim at incorporating the major results obtained through several empirical methods (in particular, via experimental work) into formal economic analysis –, I claim that rather different methodological conclusions and regulatory and policy implications can be drawn, depending on the cognitive or social nature of the departures from the standard economic model under study.     

11 Postcards from the Edge

Abstract

Environmental issues, while of growing interest, have been outside the main focus of business scholarship. This position on the periphery may have been a good thing. It allowed scholars of business and the environment to consider unusual theories and evaluate overlooked phenomenon. In doing so, they have created a body of research providing new insights into two topics of mainstream interest: (a) the sources of competitive advantage and (b) the origin and function of self-regulatory institutions.     

Dimension reduction for model-based clustering

We introduce a dimension reduction method for visualizing the clustering structure obtained from a finite mixture of Gaussian densities. Information on the dimension reduction subspace is obtained from the variation on group means and, depending on the estimated mixture model, on the variation on group covariances. The proposed method aims at reducing the dimensionality by identifying a set of linear combinations, ordered by importance as quantified by the associated eigenvalues, of the original features which capture most of the cluster structure contained in the data. Observations may then be projected onto such a reduced subspace, thus providing summary plots which help to visualize the clustering structure. These plots can be particularly appealing in the case of high-dimensional data and noisy structure. The new constructed variables capture most of the clustering information available in the data, and they can be further reduced to improve clustering performance. We illustrate the approach on both simulated and real data sets.     

An Assessment of the Psychometric Properties of Italian Version of CPGI

The aim of this study was to adapt to the Italian context a very commonly used international instrument to detect problem gambling, the canadian problem gambling index (CPGI), and assess its psychometric properties. Cross-cultural adaptation of CPGI was performed in several steps and the questionnaire was administered as a survey among Italian general population (n = 5,292). Cronbach’s alpha reliability coefficient was 0.87 and can be considered to be highly reliable. Construct validity was assessed first by means of a principal component analysis and then by means of confirmatory factor analysis, showing that only one factor, problem gambling, was extracted from the CPGI questionnaire (an eigenvalues of 4,684 with percentage of variance 52 %). As far as convergent validity is concerned, CPGI was compared with Lie/Bet questionnaire, a two-item screening tool for detecting problem gamblers, and with both depression and stress scales. A short form DSM-IV CIDI questionnaire was used for depression and VRS scale, a rating scale, was used for rapid stress evaluation. A strong convergent validity with these instruments was found and these findings are consistent with past research on problem gambling, where another way to confirm the validity is to determine the extent to which it correlates with other qualities or measures known to be directly related to problem gambling. In sum, despite the lack of a direct comparison with a classic gold-standard such as DSM-IV, the Italian version of CPGI exhibits good psychometric properties and can be used among the Italian general population to identify at-risk problem gamblers.     

The Geography of Economics and Happiness: Spatial Patterns in the Effects of Economic Conditions on Well-Being

This paper investigates the cross-country distribution of the relationship between economic conditions and well-being. Using a large sample of individuals from 94 countries worldwide, we find that the effect of income on well-being is larger in countries with lower GDP per capita, while the negative effect of being unemployed is stronger in countries with higher unemployment rate or higher GDP per capita. Interestingly, the effect of being unemployed displays positive spatial dependence across countries that is not accounted for by aggregate socio-economic conditions. Overall, the results indicate that geography, culture and institutions must be explicitly taken into account in order to understand the relationship between economic conditions and well-being.     

Robust likelihood functions in Bayesian inference

In order to deal with mild deviations from the assumed parametric model, we propose a procedure for accounting for model uncertainty in the Bayesian framework. In particular, in the derivation of posterior distributions, we discuss the use of robust pseudo-likelihoods, which offer the advantage of preventing the effects caused by model misspecifications, i.e. when the underlying distribution lies in a neighborhood of the assumed model. The influence functions of posterior summaries, such as the posterior mean, are investigated as well as the asymptotic properties of robust posterior distributions. Although the use of a pseudo-likelihood cannot be considered orthodox in the Bayesian perspective, it is shown that, also through some illustrative examples, how a robust pseudo-likelihood, with the same asymptotic properties of a genuine likelihood, can be useful in the inferential process in order to prevent the effects caused by model misspecifications.     

Latent class models for financial data analysis: some statistical developments

I exploit the potential of latent class models for proposing an innovative framework for financial data analysis. By stressing the latent nature of the most important financial variables, expected return and risk, I am able to introduce a new methodological dimension in the analysis of financial phenomena. In my proposal, (i) I provide innovative measures of expected return and risk, (ii) I suggest a financial data classification consistent with the latent risk-return profile, and (iii) I propose a set of statistical methods for detecting and testing the number of groups of the new data classification. The results lead to an improvement in both risk measurement theory and practice and, if compared to traditional methods, allow for new insights into the analysis of financial data. Finally, I illustrate the potentiality of my proposal by investigating the European stock market and detailing the steps for the appropriate choice of a financial portfolio.