Multiple-membership multiple-classification models for social network and group dependences

The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications

The Social Climbing Game

The structure of a society depends, to some extent, on the incentives of the individuals they are composed of. We study a stylized model of this interplay, that suggests that the more individuals aim at climbing the social hierarchy, the more society's hierarchy gets strong. Such a dependence is sharp, in the sense that a persistent hierarchical order emerges abruptly when the preference for social status gets larger than a threshold. This phase transition has its origin in the fact that the presence of a well defined hierarchy allows agents to climb it, thus reinforcing it, whereas in a "disordered" society it is harder for agents to find out whom they should connect to in order to become more central. Interestingly, a social order emerges when agents strive harder to climb society and it results in a state of reduced social mobility, as a consequence of ergodicity breaking, where climbing is more difficult.Comment: 14 pages, 9 figure

Social Preferences, Skill Segregation and Wage Dynamics

We study the earning structure and the equilibrium asignment of workers to firms in a model in which workers have social preferences, and skills are perfectly substitutable in production. Firms offer long-term contracts, and we allow for frictions in the labour market in the form of mobility costs. The model delivers specific predictions about the nature of worker flows, about the characteristic of workplace skill segregation, and about wage dispersion both within and cross firms. We shows that long-term contracts in the resence of social preferences associate within-firm wage dispersion with novel "internal labour market" features such as gradual promotions, productivity-unrelated wage increases, and downward wage flexibility. These three dynamic features lead to productivity-unrelated wage volatily within firms.Publicad

Measuring UK crime gangs: a social network problem

This paper describes the output of a study to tackle the problem of gang-related crime in the UK; we present the intelligence and routinely-gathered data available to a UK regional police force, and describe an initial social network analysis of gangs in the Greater Manchester area of the UK between 2000 and 2006. By applying social network analysis techniques, we attempt to detect the birth of two new gangs based on local features (modularity, cliques) and global features (clustering coefficients). Thus for the future, identifying the changes in these can help us identify the possible birth of new gangs (sub-networks) in the social system. Furthermore, we study the dynamics of these networks globally and locally, and have identified the global characteristics that tell us that they are not random graphs—they are small world graphs—implying that the formation of gangs is not a random event. However, we are not yet able to conclude anything significant about scale-free characteristics due to insufficient sample size. A final analysis looks at gang roles and develops further insight into the nature of the different link types, referring to Klerks' 'third generation' analysis, as well as a brief discussion of the potential UK policy applications of this work

Methods to Identify Linear Network Models: A Review

In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data

Preparation and characterisation of a novel redox polymer based on salicyl-N-phenylene diamine

Abstract. In this paper we introduce and study a model that considers the job market as a two-sided matching market, and accounts for the importance of social contacts in finding a new job. We assume that workers learn only about positions in firms through social contacts. Given that information structure, we study both static properties of what we call locally stable matchings, a solution concept derived from stable matchings, and dynamic properties through a reinterpretation of Gale-Shapley’s algorithm as myopic best response dynamics. We prove that, in general, the set of locally stable matching strictly contains that of stable matchings and it is in fact NP-complete to determine if they are identical. We also show that the lattice structure of stable matchings is in general absent. Finally, we focus on myopic best response dynamics inspired by the Gale-Shapley algorithm. We study the efficiency loss due to the informational constraints, providing both lower and upper bounds.

Influence networks and public goods

We consider a model of local public goods in a random network context. The influence network determines (exogenously) who observes whom every period and comprises a wide array of options depending on the degree distribution and the in/out-degree correlations. We show that there exists a unique equilibrium level of public good provision and compare it with the efficient level. We derive further insights for this problem by performing a comparative statics analysis