Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

Studies assume that socioeconomic status determines individuals’ states of health, but how does health determine socioeconomic status? And how does this association vary depending on contextual differences? To answer this question, our study uses an additive Bayesian Networks model to explain the interrelationships between health and socioeconomic determinants using complex and messy data. This model has been used to find the most probable structure in a network to describe the interdependence of these factors in five European welfare state regimes. The advantage of this study is that it offers a specific picture to describe the complex interrelationship between socioeconomic determinants and health, producing a network that is controlled by socio-demographic factors such as gender and age. The present work provides a general framework to describe and understand the complex association between socioeconomic determinants and health.     

Does religion foster generosity?

This paper reviews recent studies that claim to provide support, through statistical analysis of survey data, for the traditional proposition that being religious makes people more generous. The studies have serious shortcomings. First, the data consist exclusively of self-reports. Second, the dependent and independent variables are conceptually problematic and ill-defined. Third, even if there is a positive correlation between religious involvement and personal generosity, it may be due to selection bias. Thus, these studies do not provide serious evidence for the traditional hypothesis. Moreover, it has been directly controverted by experimental studies of economic and other behaviors.     

Demography of Illicit Emigration from China: A Sending Country's Perspective

This paper examines the demographic trends of international migration from China, particularly from Fujian Province in the 1980s and 1990s, comparing the characteristics of emigrants with nonemigrants. This research yields two major findings. First, Fujian Province became a leading immigrant-sending province in the mid-1990s. Second, there have also been changes in socioeconomic status (SES) selectivity of emigration from Fujian from 1990 to 1995. In particular, this change in emigration selectivity is characterized by a significant shift from urbanites to rural peasants. The future prospects of emigration from Fujian and China are explored.     

Multilevel models for social networks: Hierarchical Bayesian approaches to exponential random graph modeling

In many applications, researchers may be interested in studying patterns of dyadic relationships that involve multiple groups, with a focus on modeling the systematic patterns within groups and how these structural patterns differ across groups. A number of different models – many of them potentially quite powerful – have been developed that allow for researchers to study these differences. However, as with any set of models, these are limited in ways that constrain the types of questions researchers may ask, such as those involving the variance in group-wise structural features. In this paper, we demonstrate some of the ways in which multilevel models based on a hierarchical Bayesian approach might be used to further develop and extend existing exponential random graph models to address such constraints. These include random coefficient extensions to the standard ERGM for sets of multiple unconnected or connected networks and examples of multilevel models that allow for the estimation of structural entrainment among connected groups. We demonstrate the application of these models to real-world and simulated data sets.     

Women's Health Care

No abstract available for this article.     

Random alliance networks

The morphological properties of kinship and marriage alliance networks, such as circuits, are typically considered as indicators of sociological phenomena — yet, they may also be partly coincidental. To assert the contribution of chance to these morphological features, we develop a standardized method where empirical alliance networks are compared with a random baseline. We apply our framework to a variety of empirical cases and show that some corpuses are remarkably well reconstructed by our random model, while others still feature significant divergencies which may be partly connected to field-based experience. On the whole, our approach may be used to scrutinize the matrimonial role of social groups as asserted by native or ethnological theory.     

Exponential random graph models for multilevel networks

Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (0125 and 0120) to demonstrate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs.     

Using a hybrid data collection tool: Analysis of youth labour market trajectories integrating quantitative,qualitative and social network data

This article explains the factors that led to the use of a hybrid data collection tool—i.e., a single questionnaire designed to collect both quantitative and qualitative data—in two different research projects that focused on the relation between the labour market trajectories of young people and the use and accumulation of network social capital in these trajectories. The article describes the characteristics of this tool and the methodological challenges posed by its use. The advantages and disadvantages of this specific type of data collection are also addressed. Among the main advantages, the improvement of data quality and the overarching picture obtained of the career path and use of personal networks are highlighted. The most important downside found is the amount of time needed in the interview, which shows the need to pay attention to the balance between quantitative and qualitative elements in the structure of the questionnaire.     

Understanding the self-organization of occupational sex segregation with mobility networks

Decades after the beginning of the gender revolution, most women and men still work in sex-typed occupations. This is a primary driver of the gender wage gap. Most research describing the patterns of occupational sex segregation focuses on supposedly innate job characteristics that match gender stereotypical abilities and preferences, such as the use of mathematical skills or social skills, on income and status differences between occupations, and on organizational job characteristics, for example, the need to work long hours. However, beyond such occupational attributes, sex segregation is hypothesized to exhibit emergent patterns that are linked to the interdependent job mobility of women and men, in particular, men selectively leaving feminizing occupations. Developing new tools inspired by statistical network research, and using representative, longitudinal data that contain detailed occupational mobility from the UK between 2000 and 2008, this replacement mechanism is analyzed. It is shown that 19–28% of observed sex segregation is linked to this emergent phenomenon in a statistical model that disentangles the various predictors of the allocation of women and men to different occupations. This makes it the most important predictor of segregation in contrast to concurrently modelled explanations based on occupational characteristics.Data and materials availabilityThe BHPS data and the LFS data are available from the UK Data Service (https://www.ukdataservice.ac.uk/). The O*NET data is available from the O*NET homepage (https://www.onetcenter.org/). Software implemented in the environment R and code for data analysis are available upon request from the authors.