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Lawmakers are routinely confronted by urgent social issues, yet they hold conflicting policy preferences, incentives, and goals that can undermine collaboration. How do lawmakers collaborate on solutions to urgent issues in the presence of conflicts? I argue that by building mutual trust, networks provide a mechanism to overcome the risks conflict imposes on policy collaboration. But, in doing so, network dependence constrains lawmakers' ability to react to the problems that motivate policy action beyond their immediate connections. I test this argument using machine learning and longitudinal analysis of federal crime legislation co-sponsorship networks between 1979 and 2005, a period of rising political elite polarization. Results show that elite polarization increased the effects of reciprocal action and prior collaboration on crime legislation co-sponsorships while suppressing the effect of violent crime rates. These relationships vary only marginally by political party and are pronounced for ratified criminal laws. The findings provide new insights to the role of collaboration networks in the historical development of the carceral state and elucidate how political actors pursue collective policy action on urgent issues in the presence of conflict.

Abstract
Largely overlooked in research on criminal legal expansion is the rise of political polarization and its attendant consequences for crime policy. Drawing on theories of intergroup collaboration and policymaking research, I argue that network polarization—low frequencies of collaborative relations between lawmakers belonging to distinct political groups—negatively affects crime legislation passage by reducing information flows, increasing intergroup hostility, and creating opportunities for political attacks. To evaluate this perspective, I recreate dynamic legislative networks between 1979 and 2005 using data on 1,897,019 cosponsorship relationships between 1537 federal lawmakers and the outcomes of 5950 federal crime bills. Results illustrate that increases in partisan network segregation and the number of densely clustered subgroups both have negative effects on bill passage. These relationships are not moderated by majority party status and peak during the 1990s and early 2000s, a period when prison growth showed its first signs of slowing. These findings provide new insight to the relationship between polarization and policy and suggest that increases in network polarization may be partly responsible for declines in crime policy adoption observed in recent decades.

Mediation analysis is increasingly used in the social sciences. Extension to social network data, however, has proved difficult because statistical network models are formulated at a lower level of analysis (the dyad) than many outcomes of interest. This study introduces a general approach for micro-macro mediation analysis in social networks. The author defines the average mediated micro effect (AMME) as the indirect effect of a network selection process on an individual, group, or organizational outcome through its effect on an intervening network variable. The author shows that the AMME can be nonparametrically identified using a wide range of common statistical network and regression modeling strategies under the assumption of conditional independence among multiple mediators. Nonparametric and parametric algorithms are introduced to generically estimate the AMME in a multitude of research designs. The author illustrates the utility of the method with an applied example using cross-sectional National Longitudinal Study of Adolescent to Adult Health data to examine the friendship selection mechanisms that indirectly shape adolescent school performance through their effect on network structure.

AbstractPolicy responsiveness to public opinion is necessary for democratic governance, yet research on crime policy responsiveness has focused on the public at large, overlooking variation in policy support among demographic bodies and its heterogeneous effects on policy implementation. Creating new state-level measures of group opinion on punitive policy mood using 257,356 responses to 79 national surveys administered between 1970 and 2015, this study examines the effect of race, age, and gender-specific opinion on the incarceration rate. Consistent with prior studies, results reveal parallel trends in punitive policy support, with punitiveness increasing in tandem across demographic subgroups. Statistical findings from error correction models, however, reveal that not all groups had equal influence. Only the opinions of the most punitive groups—whites, men, and 30–44-year-olds—influenced the incarceration rate. Further, the largest divide in policy responsiveness to group opinion was between blacks and whites. In fact, estimates suggest that if whites' punitive policy support had not risen, the current incarceration rate would be roughly the same as if violent crime had not risen during the 45 years of mass incarceration. The implications of these results are discussed for research on policy responsiveness, partisan politics, and for the "democracy at work" hypothesis of mass incarceration.

Panel data analysis is common in the social sciences. Fixed effects models are a favorite among sociologists because they control for unobserved heterogeneity (unexplained variation) among cross-sectional units, but estimates are biased when there is unobserved heterogeneity in the underlying time trends. Two-way fixed effects models adjust for unobserved time heterogeneity but are inefficient, cannot include unit-invariant variables, and eliminate common trends: the portion of variance in a time-varying variable that is invariant across cross-sectional units. This article introduces a general panel model that can include unit-invariant variables, corrects for unobserved time heterogeneity, and provides the effect of common trends while also allowing for unobserved unit heterogeneity, time-varying coefficients, and time-invariant variables. One-way and two-way fixed effects models are shown to be restrictive forms of this general model. Other restrictive forms are also derived that offer all the usual advantages of one-way and two-way fixed effects models but account for unobserved time heterogeneity. The author uses the models to examine the increase in state incarceration rates between 1970 and 2015.

This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model—even those uncorrelated with other predictors—or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot be interpreted as effect sizes or compared between models and homophily coefficients, as well as other interaction coefficients, cannot be interpreted as substantive effects in most ERGM applications. We conduct a series of simulations considering the substantive impact of these issues, revealing that realistic levels of residual variation can have large consequences for ERGM inference. A flexible methodological framework is introduced to overcome these problems. Formal tests of mediation and moderation are also proposed. These methods are applied to revisit the relationship between selective mixing and triadic closure in a large AddHealth school friendship network. Extensions to other classes of statistical work models are discussed.

AbstractAdefining characteristic of mass incarceration is the overwhelming racial disparity in US prison populations. Yet, little research has examined the role of public attitudes in the growth of racial disparity in incarceration rates during the prison boom. This article considers the influence of explicit and modern forms of prejudice and traces a portion of the rise in racial disparity in incarceration rates to historically high levels of fear of crime. It analyzes roughly forty years of annual data on incarceration at the state level, leveraging recent methodological developments for polling data to construct longitudinal measures of state public opinion from 386,751 individual survey responses to a pooled sample of 102 national surveys. Results from panel models show that public fear of crime played a larger role in explaining rising racial disparity in incarceration rates than explicit prejudice or laissez-faire racial attitudes. Further analyses demonstrate that fear of crime mediates a sizable portion of the effect of race-specific offending. These findings reveal that it is fear of crime and its radicalized overtones, rather than explicit or laissez-faire racial attitudes, that contributes to much of the observed racial inequality in the criminal justice system.

AbstractMeasures of bipartite network structure have recently gained attention from network scholars. However, there is currently no measure for identifying key players in two-mode networks. This article proposes measures for identifying key players in bipartite networks. It focuses on two measures: fragmentation and cohesion centrality. It extends the centrality measures to bipartite networks by considering (1) cohesion and fragmentation centrality within a one-mode projection, (2) cross-modal cohesion and fragmentation centrality, where a node in one mode is influential in the one-mode projection of the other mode, and (3) cohesion and fragmentation centrality across the entire bipartite structure. Empirical examples are provided for the Southern Women's data and on the Ndrangheta mafia data.

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past 10 years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, yielding inconsistent model estimates and problematizing inference from parameters. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor (VIF) from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the VIFs for each model. The distribution of VIFs is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between VIFs and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity, and guidelines for interpretation are discussed.

Abstract
Despite decades of crime decline, police surveillance has continued to expand through a range of tactics oriented towards policing social disadvantage. Yet, despite attention to the linkages between residential inequality and policing, few studies have accounted for two intertwined structural developments since the turn of the 21st century: (1) the shift away from spatially concentrated patterns of racial segregation within urban centers towards sprawling patterns of economic segregation and (2) the turn from reactive policing towards proactive surveillance. Using the case of big data policing, we create a new measure of big data surveillance in metropolitan areas to examine how changes in segregation have affected the expansion of proactive police surveillance. In contrast to theoretical accounts emphasizing the role of police surveillance in governing economic inequality and perpetuating racial segregation, we do not find evidence that racial segregation or income inequality increase big data surveillance. Instead, much of the recent rise in big data policing is explained by increases in sprawling patterns of income segregation. These results provide new insight into the linkages between policing and residential inequality and reveal how changes in metropolitan segregation influence criminal justice surveillance in the era of big data.

Cryptomarkets—online markets for illegal goods—have revolutionized the illegal drug trade, constituting about 10% of all drug trades and attracting users to a greater variety of and more addictive substances than available in offline drug markets. This review introduces the burgeoning area of sociology research on illegal cryptomarkets, particularly in the realm of drug trade. We emphasize the expanding role of illicit online trade and its relevance for understanding broader exchange challenges encountered in all illegal trade settings. Examining the effects of online illegal trade on consumption and supply-side policing, we also discuss the harm and potential benefits of moving drug exchange from offline to online markets. We argue for a network perspective's efficacy in this research domain, emphasizing its relevance in assessing trade and discussion networks, technical innovation, and market evolution and vulnerabilities. Concluding, we outline future research areas, including market culture, failure, and the impact of online illegal trade on stratification.

Abstract
While economic sociology research and theory argue that excessive network embeddedness depresses competition in illegal markets, prior research does not examine how distinct types of embeddedness may have asymmetric effects on the diversity of purchasing behavior—the range of illegal goods that buyers typically purchase. This study considers how network embeddedness can positively or negatively affect drug purchasing diversity in online drug markets by referring buyers to new vendors or "locking" buyers into recurrent trade for the same products. We analyze novel network data on 16,847 illegal drug exchanges between 7205 actors on one online illegal drug market. Consistent with hypothesized network asymmetry, buyers are more likely to purchase a new type of drug when the transaction is part of an indirect network referral. Although histories of exchange increase the overall frequency of drug purchasing, they are associated with decreases in new drug-type purchases. In the aggregate, these processes either contribute to an integrated market where buyers purchase multiple drugs from multiple vendors (in the case of referrals) or a fragmented market characterized by recurrent trade from the same vendors for the same substances (in the case of repeated trade). We discuss the implications of these findings for research on embeddedness, illegal markets, risky exchange, and drug policy.

AbstractAlthough economic sociology emphasizes the role of social networks for shaping economic action, little research has examined how network governance structures affect prices in the unregulated and high-risk social context of online criminal trade. We consider how overembeddedness—a state of excessive interconnectedness among market actors—arises from endogenous trade relations to shape prices in illegal online markets with aggregate consequences for short-term gross illegal revenue. Drawing on transaction-level data on 16 847 illegal drug transactions over 14 months of trade in a 'darknet' drug market, we assess how repeated exchanges and closure in buyer–vendor trade networks nonlinearly influence prices and short-term gross revenue from illegal drug trade. Using a series of panel models, we find that increases in closure and repeated exchange raise prices until a threshold is reached upon which prices and gross monthly revenue begin to decline as networks become overembedded. Findings provide insight into the network determinants of prices and gross monthly revenue in illegal online drug trade and illustrate how network structure shapes prices in criminal markets, even in anonymous trade environments.

AbstractCriminal networks are frequently at risk of disruption through arrest and interorganizational violence. Difficulties in designing empirical studies of criminal network recovery, however, have problematized research into network responses to disruption. In this study, we evaluate criminal network resilience by examining network recovery from disruption in an array of different criminal networks and across different disruption strategies. We use an agent‐based model to evaluate how criminal networks recover from disruption. Our results reveal the vulnerabilities and time to recovery of numerous criminal organizations, and through them, we identify which disruption strategies are most effective at damaging various criminal networks.