Choosing a gambling partner: testing a model of mutual insurance in the lab

In this study, we investigate how economic agents choose gambling partners and how paired risky choices differ from individual ones. To this aim, we develop a simple model and design a laboratory experiment that allows us to compare individual versus paired decisions across two treatments, where pairs are, respectively, exogenously and endogenously formed. In both treatments, paired subjects decide individually and independently how to allocate their wealth over a portfolio of lotteries and fully commit to share any winnings. The main result from our experiment is that whenever agents are allowed to choose a gambling partner they decide to team up with other agents who display the same degree of risk aversion as themselves. Moreover, paired choices consistently involve higher risk taking than individual choices. This finding is more evident when information on subjects’ risk attitudes is made available and when subjects team up in homogeneous pairs, thereby confirming that subjects successfully exploit the benefits of mutual insurance.     

Estimation of the Lognormal-Pareto Distribution Using Probability Weighted Moments and Maximum Likelihood

This article deals with the estimation of the lognormal-Pareto and the lognormal-generalized Pareto distributions, for which a general result concerning asymptotic optimality of maximum likelihood estimation cannot be proved. We develop a method based on probability weighted moments, showing that it can be applied straightforwardly to the first distribution only. In the lognormal-generalized Pareto case, we propose a mixed approach combining maximum likelihood and probability weighted moments. Extensive simulations analyze the relative efficiencies of the methods in various setups. Finally, the techniques are applied to two real datasets in the actuarial and operational risk management fields.     

An Ethical Interpretation of the Nash Choice Rule

This paper provides an ethical intepretation of the Nash choice rule. In a setting in which (cardinal) utilities are interpersonally comparable, this procedure is characterised by an impartiality requirement and by the assumption that choices are not responsive to the agents' relative ability to convert resources into utility.     

On boosting kernel density methods for multivariate data: density estimation and classification

Statistical learning is emerging as a promising field where a number of algorithms from machine learning are interpreted as statistical methods and vice-versa. Due to good practical performance, boosting is one of the most studied machine learning techniques. We propose algorithms for multivariate density estimation and classification. They are generated by using the traditional kernel techniques as weak learners in boosting algorithms. Our algorithms take the form of multistep estimators, whose first step is a standard kernel method. Some strategies for bandwidth selection are also discussed with regard both to the standard kernel density classification problem, and to our 'boosted' kernel methods. Extensive experiments, using real and simulated data, show an encouraging practical relevance of the findings. Standard kernel methods are often outperformed by the first boosting iterations and in correspondence of several bandwidth values. In addition, the practical effectiveness of our classification algorithm is confirmed by a comparative study on two real datasets, the competitors being trees including AdaBoosting with trees.     

Nash bargaining theory when the number of alternatives can be finite

Nash (1950) considered a domain of convex bargaining problems. We analyse domains including, or even consisting of, finite problems and provide various characterisations of the Nash Bargaining Solution (NBS). In particular, we extend Kaneko's (1980) results. Received: 12 July 1996 / Accepted: 6 February 1997     

The virtual patient encounter--units of service in the electronic age

A patient, preoperative evaluation center can reduce delays and cancellations, but it's critical to measure staff workload and determine the best ways to coordinate care. See how one clinic in Ohio is succeeding with this effort.     

Untangling the commonalities and differences between domestic cross-regional experience and international experience in shaping speed of internationalization

Prior studies have paid attention to the influence of domestic experience on internationalization, for which domestic experience primarily refers to the accumulation of internationalization knowledge from industrial peers or partners at home. We argue that the commonalities and differences between international experience and domestic cross-regional experience have not been fully incorporated in previous frameworks in the literature. Thus, in untangling the commonalities, we purposively differentiate domestic experience into two dimensions, i.e., repetition-based experience and diversity-based experience, to investigate the contingent role of making domestic cross-regional investments in shaping the relationship between international experience and the speed of internationalization. We expect that these two dimensions of domestic experience will moderate the relationship between international experience and the speed of internationalization in a different way. More specifically, repetition-based experience has a negative moderating effect while diversity-based experience has a positive moderating effect. Further, considering the differences between international investments and domestic investments, we expect that the joint impacts of international experience and domestic cross-regional experience on internationalization speed are contingent on whether firms have sufficient resources to support multiple learning or whether institutions between the home country and foreign country is similar enough for cross-context application. We expect that financial slack and institutional distance between prior foreign entries' country and the home country play significant three-way moderating roles in setting boundaries for the relationship between domestic experience and international experience. Based on the analysis of 302 Chinese publicly listed firms from 2001 to 2014, the dynamic panel data regression results support our hypotheses. Overall, our simultaneous consideration of commonalities and differences between domestic investments and international investments sheds light on how MNCs learn from both international and domestic investments to speed up their foreign expansions.     

Labour Force Transitions among Married Women in the USA

This paper describes the patterns of labour market transitions for a cohort of married women in the USA drawn from the National Longitudinal Survey and observed between 1968 and 1991. The empirical analysis of labour market movements is motivated by human capital theory augmented by demographic and life-cycle considerations. These movements are investigated by estimating competing risk models of labour force spell duration. The results show that the determinants of spell lengths and the determinants of the reasons for spell terminations vary across labour market states. More importantly, both the labour market state in which an individual is observed over her work cycle and the labour market state to which she moves are relevant in shaping her spell length and her tradeable characteristics in the labour market.     

An improved class of estimators for the population mean

Starting from the Rao (Commun Stat Theory Methods 20:3325–3340, 1991) regression estimator, we propose a class of estimators for the unknown mean of a survey variable when auxiliary information is available. The bias and the mean square error of the estimators belonging to the class are obtained and the expressions for the optimum parameters minimizing the asymptotic mean square error are given in closed form. A simple condition allowing us to improve the classical regression estimator is worked out. Finally, in order to compare the performance of some estimators with the regression one, a simulation study is carried out when some population parameters are supposed to be unknown.