Secondhand social capital: Boundary spanning,secondhand closure,and individual performance

We move beyond the performance returns of individuals’ direct network connections to study the effects of “secondhand” social capital, i.e., from the networks of one’s contacts. We propose that certain colleagues may be more valuable to one’s job performance than others when their spillovers of novel information combine with spillovers of the cooperation needed to obtain that novelty. In a study of 1273 research and development employees across 16 business units, we find that the most benefit to one’s own performance comes from having ties that span business units and that also include secondhand closure (i.e., where one’s contacts are each embedded in a constrained, dense network). Bridging the organizational boundary provides the novelty; and secondhand closure provides the cooperation. Further, by examining who in the network is constraining these contacts, we are able to trace their cooperative motivation both to reputational and organizational identity concerns, which each create a spillover of cooperation toward the focal individual, who reaps the returns.     

Neighborhood co-offending networks,structural embeddedness,and violent crime in Chicago

Neighborhood disparities in crime are a persistent feature of U.S. cities. Scholars have documented that both local structural conditions and characteristics of spatially proximate communities influence neighborhood crime rates. Previous studies on neighborhood inequality in crime, however, are limited by their focus on identifying average spillover effects between pairs of spatially contiguous neighborhoods, and have neglected to consider how the broader social organization of the city influences local outcomes. This study examines the role of neighborhood-level criminal networks in shaping the distribution of crime throughout cities. Employing arrest records and survey data from the Project on Human Development in Chicago Neighborhoods, we construct a neighborhood-level co-offending network for Chicago for 2001. We use this network to investigate how a focal neighborhood’s homicide rate is influenced by its structural embeddedness within the larger inter-neighborhood co-offending network. Results indicate that a neighborhood’s embeddedness increases the local homicide rate, even after controlling for the neighborhood’s internal propensity toward crime and accounting for unobserved spatial processes.     

Models,methods and network topology: Experimental design for the study of interference

How should a network experiment be designed to achieve high statistical power? Experimental treatments on networks may spread. Randomizing assignment of treatment to nodes enhances learning about the counterfactual causal effects of a social network experiment and also requires new methodology (ex. Aronow and Samii, 2017a, Bowers et al., 2013, Toulis and Kao, 2013). In this paper we show that the way in which a treatment propagates across a social network affects the statistical power of an experimental design. As such, prior information regarding treatment propagation should be incorporated into the experimental design. Our findings justify reconsideration of standard practice in circumstances where units are presumed to be independent even in simple experiments: information about treatment effects is not maximized when we assign half the units to treatment and half to control. We also present an example in which statistical power depends on the extent to which the network degree of nodes is correlated with treatment assignment probability. We recommend that researchers think carefully about the underlying treatment propagation model motivating their study in designing an experiment on a network.     

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.     

Risk as Social Context: Immigration Policy and Autism in California1

Motivated by the dramatic increase in autism diagnoses in recent years, research into risk factors has uncovered substantial variation in autism prevalence by race/ethnicity, SES, and geography. Less studied is the connection between autism diagnosis rates and the social and political context. In this article, we link the temporal pattern of autism diagnosis for Hispanic children in California to state and federal anti‐immigrant policy, particularly ballot initiative Proposition 187, limiting access to public services for undocumented immigrants and their families. Using a population‐level data set of 1992–2003 California births linked to 1992–2006 autism case records, we show that the effects of state and federal policies toward immigrants are visible in the rise and fall of autism risk over time. The common epidemiological practice of estimating risk on pooled samples is thereby shown to obscure patterns and mis‐estimate effect sizes. Finally, we illustrate how spatial variation in Hispanic autism rates reflects differential vulnerability to these policies. This study reveals not only the spillover effects of immigration policy on children’s health, but also the hazards of treating individual attributes like ethnicity as risk factors without regard to the social and political environments that give them salience.     

Can’t We Just Live Together? New Evidence on the Effect of Relationship Status on Health

There has been a large empirical literature on the effect of marriage on health, but scant empirical evidence on the effect of cohabitation on health, although cohabitation is increasingly common. We contributed to this literature in three ways. First we explicitly modeled cohabitation distinct from marriage. Second, we included lagged health in our models to address the dynamic process of health and health-related selection into relationships. Extant literature has failed to control for lagged health risking omitted variable bias. Rather, it has controlled for general unobservable heterogeneity using fixed effects models that have relied on limited variation in relationship status over time to identify the effect of relationship status on health. Third, we employed a continuous health index that aids in estimation and inference of dynamic models. Using the Blundell and Bond dynamic panel data estimator and 18 years of the British Household Panel Survey of nearly 18,000 adults, we found that being in a relationship is good for health, but the benefits are not unique to marriage. Our finding that cohabitation is as beneficial as marriage for health was good news for health policy as changing social norms and economic instability have delayed or impaired family formation.     

Alternative estimation methods for identifying contagion effects in dynamic social networks: A latent-space adjusted approach

Contagion effects, also known as peer effects or social influence process, have become more and more central to social science, especially with the availability of longitudinal social network data. However, contagion effects are usually difficult to identify, as they are often entangled with other factors, such as homophily in the selection process, the individual’s preference for the same social settings, etc. Methods currently available either do not solve these problems or require strong assumptions. Following Shalizi and Thomas (2011), I frame this difficulty as an omitted variable bias problem, and I propose several alternative estimation methods that have potentials to correctly identify contagion effects when there is an unobserved trait that co-determines the influence and the selection. The Monte-Carlo simulation results suggest that a latent-space adjusted estimator is especially promising. It outperforms other estimators that are traditionally used to deal with the unobserved variables, including a structural equation based estimator and an instrumental variable estimator.     

Growth with Endogenous Risk of Biological Invasion

We model biological invasions as an unintended by-product of capital accumulation. We distinguish three spillover effects: (1) a negative production externality, (2) a negative or positive consumption externality and (3) an increase in the risk of future welfare loss. We also consider the implications when households self-protect by allocating income to reduce the potential damages from a biological invasion. An optimal output tax for production externalities is straightforward and can be augmented in the case of negative or positive spillover effects on consumer welfare. Policies to correct the effect of invasions on endogenous risk are more difficult to design.     

Impact of the tariff concessions of the RCEP agreement on the structure and evolution mechanism of manufacturing trade networks

In the world’s largest free trade area, the Regional Comprehensive Economic Partnership (RCEP) agreement will gradually eliminate tariffs on over 90% of member countries’ goods over the next 36 years. We construct a tariff policy effects evaluation framework based on the complex network theory. In this framework, the standard Global Trade Analysis Project (GTAP) model is recursively extended to generate forecasted data of bilateral trade. Based on this, we model the RCEP manufacturing trade networks and analyze the response of its core–periphery structure to the tariff concessions. Then, we evaluate policy effects on the evolution of trade patterns based on motif analysis. Finally, we construct separable temporal exponential family random graph models (STERGM) to explore the influence path and degree of the new tariff agreement on the evolutionary mechanisms of trade networks.     

Variegated neoliberalization: geographies,modalities, pathways

Across the broad field of heterodox political economy, ‘neoliberalism’ appears to have become a rascal concept – promiscuously pervasive, yet inconsistently defined, empirically imprecise and frequently contested. Controversies regarding its precise meaning are more than merely semantic. They generally flow from underlying disagreements regarding the sources, expressions and implications of contemporary regulatory transformations. In this article, we consider the handling of ‘neoliberalism’ within three influential strands of heterodox political economy – the varieties of capitalism approach; historical materialist international political economy; and governmentality approaches. While each of these research traditions sheds light on contemporary processes of market‐oriented regulatory restructuring, we argue that each also underplays and/or misreads the systemically uneven, or ‘variegated’, character of these processes. Enabled by a critical interrogation of how each approach interprets the geographies, modalities and pathways of neoliberalization processes, we argue that the problematic of variegation must be central to any adequate account of marketized forms of regulatory restructuring and their alternatives under post‐1970s capitalism. Our approach emphasizes the cumulative impacts of successive ‘waves’ of neoliberalization upon uneven institutional landscapes, in particular: (a) their establishment of interconnected, mutually recursive policy relays within an increasingly transnational field of market‐oriented regulatory transfer; and (b) their infiltration and reworking of the geoinstitutional frameworks, or ‘rule regimes’, within which regulatory experimentation unfolds. This mode of analysis has significant implications for interpreting the current global economic crisis.     

Bias decomposition and estimator performance in respondent-driven sampling

Respondent-Driven Sampling (RDS) is a method of network sampling that is used to sample hard-to-reach populations. The resultant sample is non-random, but different weighting methods can account for the over-sampling of (1) high-degree individuals and (2) homophilous groups that recruit members more effectively. While accounting for degree-bias is almost universally agreed upon, accounting for recruitment-bias has been debated as it can further increase estimate variance without substantially reducing bias. Simulation-based research has examined which weighting procedures perform best given underlying population network structures, group recruitment differences, and sampling processes. Yet, in the field, analysts do not have a priori knowledge of the network they are sampling. We show that the RDS sample data itself can determine whether a degree-based estimator is sufficient. Formulas derived from the decomposition of a ‘dual-component’ estimator can approximate the ‘recruitment component’ (RC) and ‘degree component’ (DC) of a sample’s bias. Simulations show that RC and DC values can predict the performance of different classes of estimators. Samples with extreme ‘RC’ values, a consequence of network homophily and differential recruitment, are better served by a classical estimator. The use of sample data to improve estimator selection is a promising innovation for RDS, as the population network features that should guide estimator selection are typically unknown.     

Austerity plan announcements and the impact on the employees’ wellbeing

This study investigates the impact of austerity plan announcements on employees’ wellbeing. We exploit the unexpected announcement of a drastic wage cut (25%) related to public sector employees in Romania. Using data from the Eurobarometer Surveys and the European Quality of Life Survey, we employ a difference-in-difference research design combined with matching, based on entropy balancing, to identify the causal effects. Our results reveal that the mere announcement of austerity measures leads to an overall drop in life satisfaction among those working in the public sector. We also show that men, and especially married individuals, are most affected by the substantial wage cut announcement. Contrary to previous research, we find that public sector employees with higher levels of education are more likely to be affected by this policy communication compared to those with only secondary or primary levels of education. Our results also suggest that the negative effect of the announcement is not persisting over time.     

Robust Spatial Analysis of Rare Crimes: An Information‐Theoretic Approach

In this paper we describe a semiparametric information‐theoretic framework for modeling the determinants of rare events aggregated at intracity areal units while allowing for various forms of error correlation structures. The approach is applied to an examination of the effects of socioeconomic and demographic macrocharacteristics of communities on the amount of violence they experience. We investigate and find evidence of some instability in these processes across types of violence and level of areal aggregation. However, we also find evidence of a stable predictor—resource deprivation—for all the types of violence analyzed and at both levels of areal aggregation considered. In addition, we find evidence of a spillover effect of a community's resource deprivation on the level of violence its neighboring areas can expect. We discuss our findings in light of their substantive, methodological, and practical implications.     

知识的空间溢出效应与溢出结构研究——以上海市区县为实证

知识具有外溢性,其可以使落后地区学习和借鉴发达地区相关的知识溢出成果。在上海这样的大都市,区县是否存在知识的高地或洼地?城市内部的知识溢出效果如何?根据区域知识的存量属性及其与邻近空间的相关性,分析知识的空间溢出,并基于技术相似度定量分析区域间知识的关联性,建立了5种知识溢出模式。对上海市17个区县的知识传播的溢出状况进行了分析,发现上海市中心城区的知识溢出依赖于科技投入,而周边地带的知识溢出呈现基于开发区以点带面的模式,其中徐汇区、闵行区、浦东新区作为科技投入以及开发区创新的优势地带,对周边的知识溢出起到了巨大的带动作用。通过上海市知识溢出结构分析,对解决现存问题和推动上海科技发展提出建议。     

DID REGIONAL ECONOMIC DIVERSITY INFLUENCE THE EFFECTS OF THE GREAT RECESSION?

Using data for U.S. counties from 2005 to 2012, we test whether higher levels of economic diversity mediated the effects of the Great Recession via four measures of stability. Spatial spillover effects are modeled by the use of the spatial Durbin estimator with heteroscedastic errors. The data generally support the central hypothesis that higher levels of diversity within a county are associated with enhanced employment stability across all counties as well as subsets of metro and nonmetro counties. Results for wage stability, however, appear to contradict our other findings. We suggest that underlying labor elasticities can bridge these apparent contradictory results. (JEL R11, R12, O47)     

American Criminology Meets Collins: Global Theory of Intellectual Change and a Policy‐Oriented Field1

Ideas from Randall Collins’s Sociology of Philosophies are applied to U.S. criminology, a policy‐oriented field and one case of differentiation out of a fragmented sociological discipline. Building on previous quantitative work, in‐depth interviews with eight prominent scholars provide the empirical material. As in philosophy, vertical network ties are important. Yet, they may take different forms, with consequences for the shape of horizontal networks and the nature of scholarship. Comparable to philosophy, horizontal network ties provide social capital and opportunities for interaction rituals that generate collective effervescence and emotional energy. Further, the nature of these interactions is dependent on the changing institutional environment in which they are embedded. Such institutional settings, themselves affected by changes in the political economy, also provide material resources, constituting dependencies that produce mediated effects and, in this policy‐oriented field, also direct effects on the nature of scholarship.     

Exploring the Contextual Determinants of Individual Attitudes toward Immigrants and Criminal Activity and their Spillover Policy Implications

Immigrants are routinely tied to a range of social problems in the policy making process in the US political system. Little is known however about the extent to which citizens hold attitudes that connect immigrants to particular social problems and whether these attitudes spill over to influence citizens’ preferences toward specific public policy alternatives that might appear to be largely independent of immigrants and immigration. Investigating the nexus between immigration and crime, we ask how Anglo whites’ contextual environments influence their propensity to link immigrants to a salient social pathology like crime. Results show that whites living in states where immigrant populations have increased most dramatically and in states with lower socioeconomic characteristics are more likely to associate immigration with increased criminal activity. Whites’ attitudes toward immigration‐induced crime has important spillover implications to the larger public policy making process as whites who view immigrants as a cause of criminal activity are more likely to support tougher criminal sentencing and the death penalty.     

I can’t smile without you: Spousal correlation in life satisfaction

This paper tests whether one partner’s happiness significantly influences the happiness of the other partner. Using 10 waves of the British Household Panel Survey, it utilizes a panel-based GMM methodology to estimate a dynamic model of life satisfaction. The use of the GMM-system estimator corrects for correlated effects of partner’s life satisfaction and solves the problem of measurement error bias. The results show that, for both genders, there is a positive and statistically significant spillover effect of life satisfaction that runs from one partner to the other partner in a couple. The positive bias on the estimated spillover effect coming from assortative mating and shared social environment at cross-section is almost offset by the negative bias coming from systematic measurement errors in the way people report their life satisfaction. Moreover, consistent with the spillover effect model, couple dissolution at t + 1 is negatively correlated with partners’ life satisfaction at t.     

Social Capital,Knowledge, and the Environment: The Effect of Interpersonal Communication on Climate Change Knowledge and Policy Preferences

Abstract

This article contributes to the literature on social capital and the environment in two ways. First, we identify a relationship between interpersonal discussion networks and assessed and perceived individual scientific knowledge. We then examine if knowledge mediates the relationship between discussion networks and policy preferences about climate change (CC). Using nationally representative survey data, we examine the extent to which an individual’s interpersonal discussion network is related to knowledge on CC and support for CC policy. We find those who discuss climate have higher levels of perceived knowledge, but discussion is not related to individuals’ levels of assessed knowledge. We also find that knowledge does not fully mediate the relationship between discussion and policy preferences, implying that interpersonal discussion may influence CC policy preferences independent of perceived and assessed knowledge. We conclude with remarks about the importance of knowledge perception as well as offer some avenues to extend this research.     

How do new members affect the relationship between principal investigator’s network position and academic output of granted funds?

The paper focuses on how to improve academic output of a granted fund when the adding of new members changes principal investigator’s network position. This objective is refined by exploring how new members affect the relationship between principal investigator’s network position and the academic output of granted funds, and whether this effect is similar in high-output and low-output collaboration networks. New members are divided into two groups, namely, international collaboration and inbound mobility. Using negative binomial regression on research funds supported by the National Natural Science Foundation of China, the results indicate that new members indeed have moderating effects on the relationship between principal investigator’s network position and the academic output of granted funds. Moreover, we find that this moderating effect in high-output collaboration networks is larger than that in low-output collaboration networks. Our findings provide practical implications for the decision makers to design funding planning and for the principal investigators to improve academic output.