Lorenz non-consistent welfare and inequality measurement

Typical welfare and inequality measures are required to be Lorenz consistent which guarantees that inequality decreases and welfare increases as a result of a progressive transfer. We explore the implications for welfare and inequality measurement of substituting the weaker absolute differentials and deprivation quasi-orderings for the Lorenz quasi-ordering. Restricting attention to distributions of equal means, we show that the utilitarian model - the so-called expected utility model in the theory of risk - does not permit one to make a distinction between the views embedded in the differentials, deprivation and Lorenz quasi-orderings. In contrast it is possible within the dual model of M. Yaari (Econometrica 55 (1987), 99–115) to derive the restrictions to be placed on the weighting function which guarantee that the corresponding welfare orderings are consistent with the differentials and deprivation quasi-orderings respectively. Finally we drop the equal mean condition and indicate the implications of our approach for the absolute ethical inequality indices.     

Bayesian estimation of the network autocorrelation model

The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of the network autocorrelation parameter and poor coverage of confidence intervals. In this paper, we develop new Bayesian techniques for the network autocorrelation model that address the issues inherent to maximum likelihood estimation. A key ingredient of the Bayesian approach is the choice of the prior distribution. We derive two versions of Jeffreys prior, the Jeffreys rule prior and the Independence Jeffreys prior, which have not yet been developed for the network autocorrelation model. These priors can be used for Bayesian analyses of the model when prior information is completely unavailable. Moreover, we propose an informative as well as a weakly informative prior for the network autocorrelation parameter that are both based on an extensive literature review of empirical applications of the network autocorrelation model across many fields. Finally, we provide new and efficient Markov Chain Monte Carlo algorithms to sample from the resulting posterior distributions. Simulation results suggest that the considered Bayesian estimators outperform the maximum likelihood estimator with respect to bias and frequentist coverage of credible and confidence intervals.     

The impact of taxes and transfer payments on the distribution of income: A parametric comparison

The Luxembourg Income Study data is used to explore the impact of taxes and transfer payments on the distribution of income across 13 countries for different years. The five-parameter generalized beta distribution and 10 of its special cases are considered as models for the size distribution of income. Maximum likelihood methods are used to estimate the model with corresponding measures of goodness of fit and inequality reported. These results identify the best-fitting two-, three-, and four-parameter models as well as describe the inter-temporal patterns of inequality corresponding to earnings, total income, and disposable income. A general pattern of increasing inequality is observed for almost all countries considered along with significantly different distributional impacts of taxes and transfer payments across countries.     

基于千户调查的广州城乡居民基尼系数分析

根据广州统计年鉴资料和课题组千户调查数据,运用洛伦兹曲线的函数关系式模型,并借助回归方法估计模型参数,分别计算连续收入分布的基尼系数。研究发现：根据千户调查数据计算的广州市农村居民收入基尼系数为0.4170;广州市2011年城乡居民收入基尼系数为0.3495。这一计算结果优于根据统计年鉴计算的结果,并更符合实践和更具有解释能力。     

TESTING THE LEAP-FROG HYPOTHESIS: The impact of existing infrastructure and telecommunications policy on the global digital divide

This paper tests the 'leap-frog' hypothesis by modeling the impact of existing telecommunications infrastructure, controlling for economic, political and demographic factors, on changes in information communication technology (ICT) access for over 200 countries between 1995 and 2005. This study has significantly greater coverage than previous research, in terms of both time frame and country cases. First, the analysis demonstrates that in the first decade of the information society successful leap-frog countries are few and far between. Second, the relative distribution of personal computers, internet hosts and secure servers among the nations of the world has barely improved over the last decade. Third, contrary to received wisdom, most of the countries that might qualify as successful leap-frog countries are actually among the wealthiest in the world. Finally, while policy reform in the telecommunications sector can sometimes speed the diffusion of digital communication tools, the record of market reforms is mixed, and the overall effect of economic wealth is still paramount. In sum, a few poor countries have leapt ahead in the development of a few aspects of ICT infrastructure and use, but these relatively rare successes are more likely to be due to economic productivity than to privatization, regulatory separation and depoliticization, or market liberalization in the telecommunications sector.     

Inequality and deprivation within and between groups: An illustration of European union countries

This paper broadens the approach of Hey and Lambert (Q. J. Econ. 95,567–573 1980) regarding relative deprivation to the case in which individuals compare themselves with individuals belonging not only to their own group, but also to other groups. In this way, we obtain the average deprivation of a population in relation to another. This allows us to establish a correspondence between the decomposition of the Gini coefficient based on a partition of the population, and the decomposition of deprivation into two components. One quantifies deprivation within the subpopulation and the other deprivation between populations. An empirical illustration is given, based on the European Community Household Panel (ECHP). The authors acknowledge the financial support of the Spanish Government–MCYT, (SEC2001-1668) and would like to thank Jacques Silber and two anonymous referees for their suggestions.     

Bayesian exponential random graph models with nodal random effects

We extend the well-known and widely used exponential random graph model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the basis of our modelling algorithm. A central question in network models is the question of model selection and following the Bayesian paradigm we focus on estimating Bayes factors. To do so we develop an approximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Three data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.     

Covariate-defined latent space random effects model

Latent factor models are a useful and intuitive class of models; one limitation is their inability to predict links in a dynamic network. We propose a latent space random effects model with a covariate-defined social space, where the social space is a linear combination of the covariates as estimated by an MCMC algorithm. The model allows for the prediction of links in a network; it also provides an interpretable framework to explain why people connect. We fit the model using the Adolescent Health Network dataset and three simulated networks to illustrate its effectiveness in recognizing patterns in the data.     

Conditionally exponential random models for individual properties and network structures: Method and application

Exponential random models have been widely adopted as a general probabilistic framework for complex networks and recently extended to embrace broader statistical settings such as dynamic networks, valued networks or two-mode networks. Our aim is to provide a further step into the generalization of this class of models by considering sample spaces which involve both families of networks and nodal properties verifying combinatorial constraints. We propose a class of probabilistic models for the joint distribution of nodal properties (demographic and behavioral characteristics) and network structures (friendship and professional partnership). It results in a general and flexible modeling framework to account for homophily in social structures. We present a Bayesian estimation method based on the full characterization of their sample spaces by systems of linear constraints. This provides an exact simulation scheme to sample from the likelihood, based on linear programming techniques. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of journal articles in the field of neuroscience between 2009 and 2013.     

A Bayesian approach for the reliability of scientific co-authorship networks with emphasis on nodes

The co-authorship among members of a research group commonly can be represented by a (co-authorship) graph in which nodes represent the researchers that make up of this group and edges represent the connections between two agents (i.e., the co-authorship between these agents). Current study measures the reliability of networks by taking into consideration unreliable nodes (researchers) and perfectly reliable edges (co-authorship between two researchers). A Bayesian approach for the reliability of a network represented by the co-authorship among members of a real research group is proposed, obtaining Bayesian estimates and credibility intervals for the individual components (nodes or researchers) and the network. Weakly informative and non-informative prior distributions are assumed for those components and the posterior summaries are obtained by Monte Carlo-Markov Chain methods. The results show the relevance of an inferential approach for the reliability of scientific co-authorship network. The results also demonstrate that the contribution of each researcher is highly relevant for the maintenance of a research group. In addition, the Bayesian methodology was a feasible and easy computational implementation.     

The analysis of multiple ties in longitudinal egocentric network data: A case study on bidirectional relationships between trust and drug use

We extend multi-level models to examine single egocentric network ties to the joint analysis of paired dynamic ties. Two analytic challenges are addressed. First, inference needs to account for multiple layers of nesting: ties are nested within pairs, pairs are nested within time points, and time points are nested within egos. Second, the focus is on the relationship between two dynamic ties; specification of outcome and predictor may be difficult. Instead, we treat both ties as outcomes. Our approach is used to analyze trust and reported drug use between egos and alters over time in a Bayesian framework.     

Ranking inequality: Applications of multivariate subset selection

Inequality measures are often presented in the form of a rank ordering to highlight their relative magnitudes. However, a rank ordering may produce misleading inference, because the inequality measures themselves are statistical estimators with different standard errors, and because a rank ordering necessarily implies multiple comparisons across all measures. Within this setting, if differences between several inequality measures are simultaneously and statistically insignificant, the interpretation of the ranking is changed. This study uses a multivariate subset selection procedure to make simultaneous distinctions across inequality measures at a pre-specified confidence level. Three applications of this procedure are explored using country-level data from the Luxembourg Income Study. The findings show that simultaneous precision plays an important role in relative inequality comparisons and should not be ignored. An erratum to this article can be found at     

Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models

Social network data often involve transitivity, homophily on observed attributes, community structure, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we develop Bayesian inference for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets: liking between monks and coreaderships between Slovenian publications. We also apply it to two simulated network datasets with very different network structure but the same highly skewed degree sequence generated from a preferential attachment process. One has transitivity and community structure while the other does not. Models based solely on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but the latent cluster random effects model does.     

Gini’s nuclear family

The purpose of this paper is to justify the use of the Gini coefficient and two close relatives for summarizing the basic information of inequality in distributions of income. To this end we employ a specific transformation of the Lorenz curve, the scaled conditional mean curve, rather than the Lorenz curve as the basic formal representation of inequality in distributions of income. The scaled conditional mean curve is shown to possess several attractive properties as an alternative interpretation of the information content of the Lorenz curve and furthermore proves to yield essential information on polarization in the population. The paper also provides asymptotic distribution results for the empirical scaled conditional mean curve and the related family of empirical measures of inequality.      

Network structure influence on simulated network interventions for behaviour change

We simulated diffusion of behaviour change over fifteen real-world networks with seven network interventions under both simple and complex contagion. We found that structural network properties affect both the diffusion outcome and the relative effectiveness of the different interventions, with confounding effects that were inconsistent with results expected from mathematical analysis. These results suggest that comprehensive studies are needed to identify the effects of structural properties on diffusion in real-world networks. Further, researchers attempting to identify the effect of individual properties must measure a range of properties to avoid incorrect attribution.