Would You Mind if I Get More? An Experimental Study of the Envy Game

Envy is often the cause of mutually harmful outcomes. We experimentally study the impact of envy in a bargaining setting in which there is no conflict in material interests: a proposer, holding the role of residual claimant, chooses the size of the pie to be shared with a responder, whose share is exogenously fixed. Responders can accept or reject the proposal, with game types differing in the consequences of rejection: all four combinations of (not) self-harming and (not) other-harming are considered. We find that envy leads responders to reject high proposer claims, especially when rejection harms the proposer. Notwithstanding, maximal claims by proposers are predominant for all game types. This generates conflict and results in a considerable loss of efficiency.Social Preferences, Conflict, Experimental Economic,, Bargaining

Asymptotic Properties of the Partition Function and Applications in Tail Index Inference of Heavy-Tailed Data

The so-called partition function is a sample moment statistic based on blocks of data and it is often used in the context of multifractal processes. It will be shown that its behaviour is strongly influenced by the tail of the distribution underlying the data either in i.i.d. and weakly dependent cases. These results will be exploited to develop graphical and estimation methods for the tail index of a distribution. The performance of the tools proposed is analyzed and compared with other methods by means of simulations and examples.Comment: 31 pages, 5 figure

Heavy-tailed Phenomena and Tail Index Inference

This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model phenomena in many disciplines. The definition of heavy tails based on the theory of regular variation highlights the importance of the tail index, which indicates the existence of moments and characterises the rate at which the tail decays. Two new approaches to make inference for the tail index are proposed. The first approach employs a regression technique and constructs an estimator of the tail index. It exploits the fact that the behaviour of the characteristic function near the origin reflects the behaviour of the distribution function at infinity. The main advantage of this approach is that it utilises all observations to constitute each point in the regression, not just extreme values. Moreover, the approach does not rely on prior information on the starting point of the tail behaviour of the underlying distribution and shows excellent performance in a wide range of cases: Pareto distributions, heavy-tailed distributions with a non-constant slowly varying factor, and composite distributions with heavy tails. The second approach is motivated by the asymptotic properties of a special moment statistic, the so-called partition function. This statistic considers blocks of data and is generally used in the context of multifractality. Due to the interplay between the weak law of large numbers and the generalised central limit theorem, the asymptotic behaviour of the partition function is strongly affected by the existence of moments even for weakly dependent samples. Via a quantity, the scaling function, a graphical method to identify the existence of heavy tails is proposed. Moreover, the plot of the scaling function allows one to make inference for the underlying distribution: with infinite variance, finite variance with tail index larger than two, or all moments finite. Furthermore, since the tail index is reflected at the breakpoint of the plot of the scaling function, this gives the possibility to estimate the tail index. Both these two approaches use the entire distribution, not just the tail, to analyse the tail behaviour. This sheds a new light on the analysis of heavy-tailed distributions. At the end of this thesis, these two approaches are used to detect power laws in empirical data sets from a variety of fields and contribute to the debate on whether city sizes are better approximated by a power law or a log-normal distribution

Social status competition and the impact of income inequality in evolving social networks: an agent-based model

In this paper, we consider conspicuous consumption in a model in which individuals compare themselves to their social network neighbors in terms of the amount of a status good purchased. Individuals are heterogeneous with respect to income and can change their network links based on utility considerations. We study the impact of income inequality and income redistribution on status competition and individual welfare. We find that individuals with similar income levels tend to be connected to each other in the social network emerging in the long run. Under these circumstances, the income redistribution does not significantly affect the income share spent on the status good and the relative status of individuals. In a relatively equal society, individuals with below median income levels are better off in terms of welfare, everybody else is worse off. The aggregate effect of income redistribution on welfare is negative

Curbing the consumption of positional goods: behavioral interventions versus taxation

Little is known about whether behavioral techniques, such as nudges, can serve as effective policy tools to reduce the consumption of positional goods. We study a game, in which individuals are embedded in a social network and compete for a positional advantage with their direct neighbors by purchasing a positional good. In a series of experiments, we test four policy interventions to curb the consumption of the positional good. We manipulate the type of the intervention (either a nudge or a tax) and the number of individuals exposed to the intervention (either the most central network node or the entire network). We illustrate that, if the entire network is exposed to the intervention, both the nudge and the tax can serve as effective policy instruments to combat positional consumption. Nevertheless, taxing or nudging the most central network node does not seem to be equally effective due to the absence of spillover effects from the center to the other nodes. As for the mechanism through which the nudge operates, our findings are consistent with an explanation where nudging increases the psychological cost of the positional consumption