1,721,065 research outputs found
Language and intergroup discrimination. Evidence from an experiment
No full textLanguage is one of the most salient dimensions of ethnocultural identity and clearly marks who is and who is not a member of the group. We conduct an experiment to investigate the role of language in intergroup discrimination in the creation of social capital, here operationalised as a measure encompassing trust, trustworthiness, cooperation, and coordination.
We observe the behaviour of the members of a minority language community when they receive the instructions written in their own idiomatic language and when they receive them written in the surrounding language. We find a language e↵ect on behaviour, but this e↵ect is gender specific. When deciding in the surrounding language, participants do not treat ingroup and outgroup members di↵erently. When deciding in their own idiomatic language, females show intergroup discrimination and treat ingroup members more favourably compared to how they treat them when deciding in the surrounding language. We also observe that the behaviour participants exhibit in the experiment positively correlates with their attitudes as measured by the standard trust survey question used as a proxy for social capital
Providing revenue-generating projects under a fair mechanism: An experimental analysis
No full textThis paper considers a procedurally fair provision mechanism that allows members of a small group to determine, through their bids, which project to implement. Previous experiments with (only) costly projects have demonstrated that the mechanism is efficiency enhancing. Our experiment tests whether the mechanism remains conducive to efficiency when revenue-generating, but less efficient, projects are made available. We find that this is not the case. Additionally, we detect no significant difference in bid levels depending on whether mixed valuations are present or absent, and on whether the others' valuations are known or unknown. We interpret these results as evidence that the availability of revenue-generating projects engenders a biased perception of the efficient costly project
Group Reciprocity
Full text linkPeople exhibit group reciprocity when they retaliate, not against the person who harmed them, but against somebody else in that person's group. Group reciprocity may be a key motivation behind intergroup conflict. We investigated group reciprocity in a laboratory experiment. After a group identity manipulation, subjects played a Prisoner's Dilemma with others from different groups. Subjects then allocated money between themselves and others, learning the group of the others. Subjects who knew that their partner in the Prisoner's Dilemma had defected became relatively less generous to people from the partner's group, compared to a third group. We use our experiment to develop hypotheses about group reciprocity and its correlates.reciprocity, groups, conflict
The asymptotic loss distribution in a fat-tailed factor model of portfolio credit risk
Full text linkThis paper extends the standard asymptotic results concerning the percentage loss distribution in the Vasicek uniform model to a setup where the systematic risk factor is non-normally distributed. We show that the asymptotic density in this new setup can still be obtained in closed form; in particular, we derive the return distributions, the densities and the quantile functions when the common factor follows two types of normal mixture distributions (a two-population scale mixture and a jump mixture) and the Student’s t distribution. Finally, we present a real-data application of the technique to data of the Intesa - San Paolo credit portfolio. The numerical experiments show that the asymptotic loss density is highly flexible and provides the analyst with a VaR which takes into account the event risk incorporated in the fat-tailed distribution of the common factor.Factor model, asymptotic loss, Value at Risk.
A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data
Full text linkThis paper proposes an algorithm for the estimation of the parameters of a Logistic Auto-logistic Model when some values of the target variable are missing at random but the auxiliary information is known for the same areas. First, we derive a Monte Carlo EM algorithm in the setup of maximum pseudo-likelihood estimation; given the analytical intractability of the conditional expectation of the complete pseudo-likelihood function, we implement the E-step by means of Monte Carlo simulation. Second, we give an example using a simulated dataset. Finally, a comparison with the standard non-missing data case shows that the algorithm gives consistent results.Spatial Missing Data, Monte Carlo EM Algorithm, Logistic Auto-logistic Model, Pseudo-Likelihood.
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Spatial models for flood risk assessment
Full text linkThe problem of computing risk measures associated to flood events is extremely important not only from the point of view of civil protection systems but also because of the necessity for the municipalities of insuring against the damages. In this work we propose, in the framework of an integrated strategy, an operating solution which merges in a conditional approach the information usually available in this setup. First we use a Logistic Auto-Logistic (LAM) model for the estimation of the univariate conditional probabilities of flood events. This approach has two fundamental advantages: it allows to incorporate auxiliary information and does not require the target variables to be indepen- dent. Then we simulate the joint distribution of floodings by means of the Gibbs Sampler. Finally we propose an algorithm to increase ex post the spatial autocorrelation of the simulated events. The methodology is shown to be effective by means of an application to the estimation of the flood probability of Italian hydrographic regions.Flood Risk, Conditional Approach, LAM Model, Pseudo-Maximum Likelihood Estimation, Spatial Autocorrelation, Gibbs Sampler.
A note on maximum likelihood estimation of a Pareto mixture
Full text linkIn this paper we study Maximum Likelihood Estimation of the parameters of a Pareto mixture. Application of standard techniques to a mixture of Pareto is problematic. For this reason we develop two alternative algorithms. The first one is the Simulated Annealing and the second one is based on Cross-Entropy minimization. The Pareto distribution is a commonly used model for heavy-tailed data. It is a two-parameter distribution whose shape parameter determines the degree of heaviness of the tail, so that it can be adapted to data with different features. This work is motivated by an application in the operational risk measurement field: we fit a Pareto mixture to operational losses recorded by a bank in two different business lines. Losses below an unknown threshold are discarded, so that the observed data are truncated. The thresholds used in the two business lines are unknown. Thus, under the assumption that each population follows a Pareto distribution, the appropriate model is a mixture of Pareto where all the parameters have to be estimated.
When the State Doesn''t Play Dice: Aggressive Audit Strategies Foster Tax Compliance
No full textAccompanying data of the paper. The database presented is obtained directly from the raw zTree output and takes into account only variables that are relevant for the analysis reported in the paper. The database has 4500 rows and 6 columns
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