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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.

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.

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.

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 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.

AbstractWe explore the annual number of death sentences imposed on black and white offenders within each US state from 1989 through 2017, with particular attention to the impact of aggregate levels of racial resentment. Controlling for general ideological conservatism, homicides, population size, violent crime, institutional and partisan factors, and the inertial nature of death sentencing behavior, we find that racial hostility translates directly into more death sentences, particularly for black offenders. Racial resentment itself reflects each state's history of racial strife; we show powerful indirect effects of a history of lynching and of racial population shares. These effects are mediated through contemporaneous levels of racial resentment. Our findings raise serious questions about the appropriateness of the ultimate punishment, as they show its deep historical and contemporary connection to white racial hostility toward blacks.