Settings in Social Networks: A Measurement Model

A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive structures, expressed by ultrametrics, and (2) the expected tie strength decreases with ultrametric distance. The approach could be described as model–based clustering with an ultrametric space as the underlying metric to capture the dependence in the observations. Bayesian methods as well as maximum–likelihood methods are applied for statistical inference. Both approaches are implemented using Markov chain Monte Carlo methods.     

Asymmetric Information under the Kafala Sponsorship System: Impacts on Foreign Domestic Workers’ Income and Employment Status in the GCC Countries

This article examines the legal and policy implications of information asymmetry for foreign domestic workers employed under the Kafala sponsorship system in the Gulf Cooperation Council (GCC) countries. Drawing from ethnographic and field‐based observations in large GCC migrant destinations – including Kuwait, Qatar, and the United Arab Emirates (UAE) – we investigate the information flows and market uncertainties between five key stakeholders: labour‐receiving governments, labour‐sending governments, recruitment agencies (subagents), sponsors (employers), and social networks. Several factors contribute to asymmetric information: the lack of bilateral labour agreements and government policy coordination, programs between and among government entities, the absence of labour law for domestic workers, and the laissez faire approach of the labour‐receiving government. These sources of asymmetric information create serious market vulnerabilities for the domestic worker population, often resulting in loss of employment and early deportation. The concluding section further outlines policy implications and areas of methodological research on GCC migration.     

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 Independent Dyads (CID) network models: A latent variable approach to statistical social network analysis

Latent variable network models that accommodate edge correlations implicitly, by assuming an underlying latent factor, are increasing in popularity. Although, these models are examples of what is a growing body of research, much of the research is focused on proposing new models or extending others. There has been very little work on unifying the models in a single framework. In this paper, we present a complete framework that organizes existing latent variable network models within an integrative generalized additive model. Our framework is called Conditionally Independent Dyad (CID) models, and includes existing network models that assume dyad (or edge) independence conditional on latent variables and other components in the model. We further discuss practical aspects of model fitting such as posterior parameter estimation via MCMC, identifiability of parameters, approaches to handle missing data and model selection via cross-validation, for the proposed additive CID models. Finally, by presenting several data examples, we illustrate the utility of the proposed framework and provide advice on selecting components for building new CID models.     

Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals.     

From attitudes to social networks: National gender-role attitudes and gender differences in late-life social relationships

Studies often find gender differences in social networks in later life, but are these findings universal, or do they differ in various cultural contexts? To address this research gap, the current study examines the association between gender differences in social relationships and country-level gender-role attitudes. We combined data from the Survey of Health, Ageing and Retirement in Europe (SHARE) of individuals aged 50 years and older with country-level data on gender-role attitudes from the European Values Survey (EVS) for 15 European countries. We estimated a series of multivariate hierarchical regression models that predicted the size of the personal social network, its emotional closeness, and the proportion of the spouse, children, and friends in the network. The results indicated gender differences in social network characteristics. Women reported larger social networks and were more likely to have larger proportions of children and friends but smaller proportions of the spouse in their social networks. The magnitude of gender differences was associated with country-level gender-role attitudes. In countries with more egalitarian gender-role attitudes, women had larger networks with a larger proportion of friends compared to men. In countries with more traditional gender-role attitudes, women had larger proportions of their children and spouse in their social networks and had emotionally closer networks. Our findings suggest that the societal context and opportunity structures for social interactions play an important role in shaping the structure of women’s and men’s social relationships in later life.     

Fair Trade Flowers: Global Certification,Environmental Sustainability,and Labor Standards

This article analyzes the organization of the fair trade flower industry, integration of Ecuadorian enterprises into these networks, and power of certification to address key environmental and social concerns on participating estates. Pursuing a social regulatory approach, I locate fair trade within the field of new institutions that establish and enforce production criteria in international markets. My research finds that while firm owners and managers support fair trade's environmental and social goals, these commitments are delimited by mainstream market expectations related to production efficiency and product quality. In environmental arenas, certification helps ensure that conditions exceed legal mandates and industry norms. In social arenas, certification helps ensure that labor standards exceed legal and industry expectations and funds important programs benefiting workers and their families. Where unions are absent, fair trade's greatest impact may be in the establishment of workers' committees that can build collective capacity. Although these new labor organizations face numerous challenges, they may strengthen the social regulation of global flower networks, making firms accountable to their workers as well as to nongovernmental organizations, retailers, and consumers.     

A latent space model for cognitive social structures data

This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a relativist approach in which no assumption is made about the existence of an underlying true network. More specifically, we rely on a generalized linear model that incorporates a bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. This is a spatial model, in which the perception of each individual about the strength of the relationships can be explained by the perceived position of the actors (themselves and others) on a latent social space. A key goal of the model is to provide a mechanism to formally assess the agreement between each actors’ perception of their own social roles with that of the rest of the group. Our experiments with both real and simulated data show that the capabilities of our model are comparable with or, even superior to, other models for CSS data reported in the literature.     

The structure and vulnerability of a drug trafficking collaboration network

Prior research has established that collaboration networks exhibit heavy-tailed degree distributions, assortative degree mixing, and large clustering coefficients. Using court record data, we assess these properties in a collaboration network among heroin traffickers. Consistent with prior research, we find an exponential degree distribution and strong local clustering. However, the traffickers mix dissortatively by degree rather than assortatively. Using a graph sampling method, we show that a consequence of dissortative mixing is that targeted vertex removals have a greater impact on the connectivity and cohesion of the trafficking network. We also note the importance of degree mixing for characterizing and identifying topological weaknesses.     

Do transnational brokers always win? A multilevel analysis of social support

Migrants often maintain relationships with significant others located in their countries of origin, which results in having transnational interpersonal ties in addition to local ones. The majority of previous studies indicate that financial and social remittances flow from countries of immigration to the countries of emigration through migrants and their networks. However, less is known about who is involved in those exchanges, what kind of supportive resources flow within and across nation-state borders, and what level of individual cross-border engagement of migrants is related to those flows. We ask whether and how transnationality as an individual attribute, together with other personal, dyadic, and supradyadic characteristics, explain received social support. Drawing on data from 100 ego-centric networks collected from Turkish migrants in Germany, the results indicate that not only the dyadic level but also network structure, the position occupied by individuals in the network and their level of transnationality explain supportive resource flows within and across borders.     

Latent Class Marginal Models for Cross-Classifications of Counts

The standard latent class model is a finite mixture of indirectly observed multinomial distributions, each of which is assumed to exhibit statistical independence. Latent class analysis has been applied in a wide variety of research contexts, including studies of mobility, educational attainment, agreement, and diagnostic accuracy, and as measurement error models in social research. One of the attractive features of the latent class model in these settings is that the parameters defining the individual multinomials are readily interpretable marginal probabilities, conditional on the unobserved latent variable(s), that are often of substantive interest. There are, however, settings where the local-independence axiom is not supported, and hence it is useful to consider some form of local dependence. In this paper we consider a family of models defined in terms of finite mixtures of multinomial models where the multinomials are parameterized in terms of a set of models for the univariate marginal distributions and for marginal associations. Local dependence is introduced through the models for marginal associations, and the standard latent class model obtains as a special case. Three examples are analyzed with the models to illustrate their utility in analyzing complex cross-classifications.     

Retaliatory use of public standards in trade

This research investigates the extent to which countries use public standards as a means of political retaliation in the international policy arena. We match World Trade Organization notifications of Sanitary and Phytosanitary (SPS) standards with SPS committee data on Specific Trade Concerns and annual, bilateral trade flows to test whether countries targeted with a public standard respond with a trade barrier of their own. We find that this type of retaliatory behavior exists for economically strategic goods. Our results suggest that about 1000 bilateral trade flows globally—or just over $11.8 billion in trade—were subject to retaliatory standards in 2015.     

On the links between globalization and poverty

This article surveys the evidence on the linkages between globalization and poverty, drawing on a new NBER study that has been completed under the direction of one of the authors. We focus on two measures of globalization: trade and international capital flows. Past researchers have argued that global economic integration should help the poor since poor countries have a comparative advantage in producing goods that use unskilled labor. Our first conclusion is that such a simple interpretation of general equilibrium trade models is misleading. Second, the evidence suggests that the poor are more likely to share in the gains from globalization when there are complementary policies in place.Third, trade and foreign investment reforms have produced benefits for the poor in exporting sectors and sectors that receive foreign investment. Fourth, financial crises are very costly to the poor. Finally, the collected evidence suggests that globalization produces both winners and losers among the poor. The fact that some poor individuals are made worse off by trade or financial integration underscores the need for carefully targeted safety nets. We conclude the article by identifying a number of unanswered research questions regarding the impact of globalization on poverty.     

Taiwanese Immigrant Entrepreneurs in Canada and Transnational Social Space

This article explores the migration of Taiwanese immigrant entrepreneurs to Canada and their transnationalism. Their presence in Canada is documented and described with statistical data on their demographic characteristics, human capital, and economic capital. An assessment of their transnationalism is provided by primary qualitative data gathered through in‐depth interviews. Overall, the review of the literature on Taiwanese migration to various countries, and their transnationalism, indicates that research has primarily been conducted in Australia and the United States, while it remained understudied in Canada. The article makes a case for contextualizing Taiwanese entrepreneurial migration in terms of a global immigration marketplace and the specific business migration programmes in Australia, United States, and Canada. Further, the article argues for the appropriateness of conceptualizing these Taiwanese entrepreneurs as operating within the theoretical framework of transnational social space. The findings on transnational social space include the importance of transnational familial networks, transnational business circuits, and transmigration. Transnational familial networks constitute a form of “capital” as the dispersal is a “resource”. The transnational business circuits include three types: (1) Asian production‐North American distribution; (2) retail chains; and (3) import‐export, all spatially distributed with their multiple national sites. In selected areas of the presentation and discussion of the data the policy implications of the findings are explored. These include discussion of the implications of this transnationalism on Canadian policies such as immigration, multiculturalism, business development, international trade, economic development, and citizenship. There is clearly a lack of harmonization among migration policy and other social and economic policies in Canada. While Canadian multiculturalism policy facilitates transnationalism, Canadian citizenship policy is shown to conflict with and discourage transnational practices.     

Health perceptions of local community works: network women describe how flows of energy and space of action generate health and ill health

This paper presents a theoretical model on how flows of energy and space of action generate health and ill health in a local community work. Local community work was assessed through a case study of women's networks in a peripheral region of northern Sweden. The aim of the study was to analyse what participation in women's networks can mean for the members health perceptions. A purposeful selection of women's networks was made for the purpose of carrying out a qualitative follow-up study. Grounded theory was used to analyse the data and to generate a theoretical model. The meaning of participation in networks proved to be plural, as both health-deteriorating and health-promotive mechanisms were found. Two core categories "flows of energy" and "a space of action" as well as the four ancillary categories: social relations within the network; increased awareness of gender and power; becoming visible; and material prerequisites for networking were grounded in the data. We suggest that under certain circumstances local community work can be of crucial importance for health promotion.     

Trade shows and the creation of market and industry

This study addresses the question of the constitution of markets in advanced societies. Specifically, the article studies the role of the traveling trade show in creating the real time computing market, which is part of the US electronics sector, during the mid‐1990's. Real time computing products assist the transfer, storage and processing of digital signals in real time and support many of the internet applications we use today. By applying ethnographic methods, we explore the general question of how economic actors cope with uncertainty in the phase of market‐making and at the cutting edge of technology. The paper makes two contributions to the existing literature. First, it shows that the attempt to organize a trade show in real time computing was triggered by the uncertainty experienced by sellers regarding the identity of prospective buyers and about the exact use to which they would put the emergent technology which is offered for sale. Secondly, we trace the history of an emergent market. We claim that trade shows for innovative products are important venues at which markets coalesce. The identification and ordering of market actors, the institutionalization of a distinct business culture and the social networks developed among market actors and across the subsidiary markets provided the basic social infrastructure for what later became known as the real time computing industry.     

Who benefits from network intervention programs? TERGM analysis across ten Philippine low-income communities

Building social capital and strengthening social networks among members of low-income communities has been recommended as a potential pathway out of poverty. However, it is not clear how network-strengthening interventions and community-based programs interact with pre-existing networks and power structures. We examine the impact of one such intervention in ten low income communities in the Philippines. The intervention is a standardized program of a faith-based organization implemented in thousands of communities in multiple countries. It brings together low-income individuals in each community for 16 weekly sessions about health, income generation, and Christian values. An important but yet unmeasured goal of the intervention is the strengthening of social networks among the participants. We measured the social networks before and after the intervention and analysed their changes both separately and jointly for all ten communities with temporal exponential random graph models (TERGM). We modelled the post-intervention network structures conditioning on the pre-intervention networks, pre-intervention node attributes, and attribute changes through the intervention. We found social engagement (measured by social visits to others) to moderate most consistently the effects of the intervention across the ten communities. Those who were more socially engaged consistently strengthened their networks through the intervention. By contrast, some network mechanisms strongly diverged between the communities. In particular, religiosity was positively associated with gaining social links through this faith-based intervention in some communities and negatively in others. Similar communities may in some aspects react to the same intervention in opposite ways—a phenomenon that should be further explored through studies of larger numbers of comparable networks.     

Fast clustering for signed graphs based on random walk gap

Signed graphs are often used as models for social media mining, social networks analysis and nature language processing. In this paper, we study clustering algorithms for signed graphs that can be scaled for use in large-scale signed networks. A proposed mechanism, called a random walk gap (RWG), is used to extract more cluster structure information. RWG uses two types of random walks on signed graphs. The first considers positive edges only. The second takes negative edges into account. Three types of edges in signed graphs are identified and their different characteristics in terms of transition probability changes are determined for the two types of random walk graphs. Different characteristics contain different information about cluster structure and a RWG matrix is used to reflect the differences. The RWG matrix is used to adjust the weights of edges in signed graphs. This is the first study to perform a random walk on negative edges. A fast clustering for signed graphs (FCSG) algorithm is then proposed. This FCSG is compared with existing methods. The computational times for different algorithms are measured. The experiments show that the proposed FCSG algorithm gives better results than existing algorithms based on the performance criteria of imbalance and modularity.     

Fostering social capital in an international multi-stakeholder issue network

Scholars have theorized that public relations contributes to societies and communities by bringing attention to pressing public issues and fostering social capital in civil society networks. However, the extant research has studied civil society networks of NGOs, donors, and the media in transitional countries. This study extends the public relations model of civil society in two ways. First, it broadens the scope to an international context. Second, it draws from the multi-stakeholder issue network perspective to conceptualize a civil society network as a space where stakeholders of an issue mix their interests as they collectively address a pressing public issue. The literature on international and multi-stakeholder networks suggest that the international scope and the mixing of interests across sectors may restrict the production of social capital. The results from the social network analysis suggests that the mixing of interests across sectoral and geopolitical boundaries did not restrict the production of social capital. Rather, the patterns of the relationships among those on the core and those on the periphery of the network restricted the production of social capital. Such finding demonstrates how public relations’ functions like relationship building can have profound influences on social capital and civil society networks. The implications for public relations theorizing and research are discussed.     

The Kazakhs of Western Mongolia: transnational migration from 1990–2008

The Kazakhs are the largest minority group in Mongolia, a relatively homogenous country dominated by Khalkh Mongols. Since 1991, Mongolia has transitioned politically and economically and witnessed significant changes in internal and international migration flows. The large-scale movement of ethnic Kazakhs from Western Mongolia to Kazakhstan represents one such emerging international flow. This migration is influenced by economic motivations, historical cultural ties to Kazakhstan, and immigration policies of both countries. This paper assesses the local and national circumstances that shape migration decision-making in Western Mongolia during the transition years and highlights changes in the characteristics and directions of migration flows during this time. We identify three periods of migration with each period characterized by changing economies and national policies in Mongolia and Kazakhstan, as well as changes in communications technologies and extensiveness of social networks among prospective migrants. These periods illustrate how transnational migration flows evolve through time and are affected by national, local, and individual circumstances.