What happens if you single out? An experiment

We present an experiment investigating the effects of singling out an individual on trust and trustworthiness. We find that (a) trustworthiness falls if there is a singled out subject; (b) non-singled out subjects discriminate against the singled out subject when they are not responsible of the distinct status of this person; (c) under a negative frame, the singled out subject returns significantly less; (d) under a positive frame, the singled out subject behaves bimodally, either selecting very low or very high return rates. Overall, singling out induces a negligible effect on trust but is potentially disruptive for trustworthiness.     

Contextual Variation in Stereotype Content: An Investigation of Children's Central Tendency and Group Variability Judgements

Drawing upon self‐categorization theory, we predicted that the content of children's stereotypes concerning the gender ingroup would be contextually variable. Two studies are reported, each looking at five‐, seven‐, and 10‐year‐old children's stereotypes of the gender in‐group in two different contexts. Study 1 examined judgements of the perceived central tendency of the in‐group on specified dimensions. Study 2 addressed judgements of perceived variability within the gender in‐group on the same dimensions. Overall multivariate analysis of the data indicated that central tendency judgements were influenced by the context in which a group is considered, but that group variability judgements were not. Individual‐level analyses showed that the most typical pattern of response over the two conditions, in both Studies 1 and 2, was for children to provide consistent rather than varying judgements. However, in Study 1 (but not Study 2 ), among those children who changed their judgements, the nature of change was precisely as predicted by self‐categorization theory.     

Brief Communications: Gender Differences in Gambling Progression

The authors compared 39 women and 38 men entering an outpatient treatment program for pathological gambling. They were diagnosed according to DSM-IV and selected by SOGS, followed by a semi-structured interview for demography and progression of the gambling behavior prior to treatment. Women were more often single (59% vs. 26%; p = .005) and started gambling significantly later than men (34.2 vs. 20.4 years; p < .001). The progression of the disorder was more than 2 times faster in women than in men. There was no difference in the age of seeking treatment (44.7 vs. 42.3 years). Findings from this study resemble gender differences in other addictions—in particular the faster progression among women—challenge pharmacodynamic hypotheses for this phenomenon, and suggest gender into account when devising treatment strategies for pathological gambling.     

Diverging patterns of interaction around news on social media: insularity and partisanship during the 2018 Italian election campaign

ABSTRACT

The paper considers how social media ecologies are affecting partisan engagement around political news and online attention economies by investigating the case of the 2018 Italian general election. By analyzing Twitter and Facebook interactions around political news in the lead-up to the election, we shed light on levels of insularity characterizing sources preferred by different partisan communities and investigate how specific patterns of active attention emerge around different sources and around stories proposing different framing of specific political actors. Our findings indicate that, on Twitter, sources mainly shared by supporters of populist parties (the Five Star Movement and the League) are characterized by higher levels of insularity compared to those shared by supporters of other parties. We also find that, on Facebook, news items published by highly insular sources receive a higher number of shares per comment. Finally, our analyses show that news presenting a positive framing of the Five Star Movement – the unique ‘cyber party’ in the system – receives a higher number of shares per comment compared to items presenting the Movement in a negative light, while the opposite is true for stories on all other political parties.     

Spatial Distribution of Social Inequality in the Metropolitan District of Quito,Ecuador

Social Indicators Research - Exploring the spatial variation of regional social inequality allows the design of public policies; therefore, this study analyzes this phenomenon in the Metropolitan...     

Legalization and human capital accumulation

This paper presents new evidence regarding the effects of legalization on the training of immigrants who were granted legal status through the US Immigration Reform and Control Act (IRCA) of 1986. Our findings point to a large increase in the immigrants’ incidence of training relative to comparable groups of natives following legalization. While training gains are higher for males, wage gains are higher for females. We also show that an important part of these changes in labor market outcomes occurs through occupation changes by newly legalized immigrants.     

Algebraic Markov Bases and MCMC for Two-Way Contingency Tables

ABSTRACT.  The Diaconis–Sturmfels algorithm is a method for sampling from conditional distributions, based on the algebraic theory of toric ideals. This algorithm is applied to categorical data analysis through the notion of Markov basis. An application of this algorithm is a non-parametric Monte Carlo approach to the goodness of fit tests for contingency tables. In this paper, we characterize or compute the Markov bases for some log-linear models for two-way contingency tables using techniques from Computational Commutative Algebra, namely Gröbner bases. This applies to a large set of cases including independence, quasi-independence, symmetry, quasi-symmetry. Three examples of quasi-symmetry and quasi-independence from Fingleton ( Models of category counts , Cambridge University Press, Cambridge, 1984) and Agresti ( An Introduction to categorical data analysis , Wiley, New York, 1996) illustrate the practical applicability and the relevance of this algebraic methodology.     

The bipolar Choquet integral representation

Cumulative Prospect Theory is the modern version of Prospect Theory and it is nowadays considered a valid alternative to the classical Expected Utility Theory. Cumulative Prospect theory implies Gain-Loss Separability, i.e., the separate evaluation of losses and gains within a mixed gamble. Recently, some authors have questioned this assumption of the theory, proposing new paradoxes where the Gain-Loss Separability is violated. We present a generalization of Cumulative Prospect Theory which does not imply Gain-Loss Separability and is able to explain the cited paradoxes. On the other hand, the new model, which we call the bipolar Cumulative Prospect Theory, genuinely generalizes the original Prospect Theory of Kahneman and Tversky, preserving the main features of the theory. We present also a characterization of the bipolar Choquet Integral with respect to a bi-capacity in a discrete setting.     

Using the Reversible Jump MCMC Procedure for Identifying and Estimating Univariate TAR Models

One way that has been used for identifying and estimating threshold autoregressive (TAR) models for nonlinear time series follows the Markov chain Monte Carlo (MCMC) approach via the Gibbs sampler. This route has major computational difficulties, specifically, in getting convergence to the parameter distributions. In this article, a new procedure for identifying a TAR model and for estimating its parameters is developed by following the reversible jump MCMC procedure. It is found that the proposed procedure conveys a Markov chain with convergence properties.