The Endangered Empire: American Re- sponses to Transnational Organized Crime

Abstract

Transnational organized crime in the United States represents the dark side of globalization. This chapter attempts to identify the key problems that face US law enforcement in its efforts to cope with the challenges posed by transnational crime. While the realities of global crime require a broad, comprehensive law enforcement response, unfortunately, even in the United States, law enforcement agencies still tend to turn to local organizations and resort to local remedies to combat international criminal activity. In recent years the size of illegal markets and the resources of transnational crime networks have imposed financial burdens on governments that must devote resources to this struggle. Furthermore, the needs for flexible international trade initiatives and commercial investment frameworks makes the task of US law enforcement agencies engaged in criminal containment and control strategies more difficult to develop and sustain. US law enforcement is multi-layered with jurisdictional authority that is sometimes overlapping and reinforcing which in turn strengthens responses to crime. On the other hand, there are serious issues concerning decentralized enforcement authority that affects federal, regional, and state jurisdictions. Complicating matters still further are the external threats that transnational crime poses that lay beyond the official reach of U. S. criminal justice. Other US issues that complicate and indeed thwart adequate enforcement responses include: the politicization of law enforcement budgets and central government allocations of resources for law enforcement which too often depend upon political partisanship and party loyalties. And media-engendered fears about transnational crime phenomena are often confused with genuine social problems such as immigration and illegal aliens. Too frequently, media distort public attention about crime problems such as urban street gangs operating across national borders.     

A network centrality bias: Central individuals in workplace networks have more supportive coworkers

Combining the results of two empirical studies, we investigate the role of alters’ motivation in explaining change in ego’s network position over time. People high in communal motives, who are prone to supportive and altruistic behavior in their interactions with others as a way to gain social acceptance, prefer to establish ties with co-workers occupying central positions in organizational social networks. This effect results in a systematic network centrality bias: The personal network of central individuals (individuals with many incoming ties from colleagues) is more likely to contain more supportive and altruistic people than the personal network of individuals who are less central (individuals with fewer incoming ties). This result opens the door to the possibility that the effects of centrality so frequently documented in empirical studies may be due, at least in part, to characteristics of the alters in an ego’s personal community, rather than to egos themselves. Our findings invite further empirical research on how alters’ motives affect the returns that people can reap from their personal networks in organizations.     

Police Crime: The Criminal Behavior of Sworn Law Enforcement Officers

This essay examines the criminological conceptualizations and research on police crime, that is, the criminal behavior of sworn law enforcement officers. It starts with an overview of the conceptualization of police crime as it relates to organizational and scholarly perspectives of social deviance. Police deviance is often conceptualized to include various misconduct, corruption, and/or crime committed by police organizations and police officers. This essay focuses on police crime resulting in the arrest of a sworn law enforcement officer and is organized within a conceptual framework that incorporates five types of police crime: sex‐related police crime, alcohol‐related police crime, drug‐related police crime, violence‐related police crime, and profit‐motivated police crime.     

Informal lending networks in rural Ethiopia

Economic development in emergent nations is tied to smallholder subsistence populations whose livelihoods are vulnerable to exogenous shocks. When shocks occur, individuals often rely on resources embedded within informal insurance networks. Resource access is related to network position and reflected in properties such as centrality and reachability. We analyze a complete informal lending network (188 nodes, 295 ties) among the Sidama, an agro-pastoralist population in southwestern Ethiopia. Results indicate that culturally salient indicators of wealth, such as cattle ownership and gender, largely account for network structure. Analysis of a complete network further allows us to discuss the impact of global network properties, such as overall typology, on a communities response to different types of shocks (covariate and idiosyncratic). These findings extend our understanding of how individuals and communities engage informal lending networks in response to exogenous shocks.     

The economics of organized crime and optimal law enforcement

This article extends the optimal law enforcement literature to organized crime. I model the criminal organization as a vertical structure where the principal extracts some rents from the agents through extortion. As long as extortion is a costless transfer from individuals to the criminal organization, not only the existence of extortion is social welfare improving because it makes engaging in a criminal offense less attractive but it also allows the government to reduce expenditures on law enforcement. When extortion is costly because the criminal organization resorts to threats and violence, the existence of extortion is social welfare diminishing and may lead to higher expenditures on law enforcement.     

Centrality in affiliation networks

This paper discusses the conceptualization, measurement, and interpretation of centrality in affiliation networks. Although centrality is a well-studied topic in social network analysis, and is one of the most widely used properties for studying affiliation networks, virtually all discussions of centrality and centralization have concerned themselves with one-mode networks. Bonacich's work on simultaneous group and individual centralities is a notable exception (Social Networks, 1991, 13, 155–168). I begin by outlining the distinctive features of affiliation networks and describe four motivations for centrality indices in affiliation networks. I then consider properties of some existing centrality indices for affiliation networks, including the relationship between centralities for actors and events in these networks, and present a new conceptualization of centrality that builds on the formal properties of affiliation networks and captures important theoretical insights about the positions of actors and events in these networks. These centralities are then illustrated on Galaskiewicz's data on club and board memberships of a sample of corporate executive officers (Social Organization of an Urban Grants Economy. New York: Academic Press, 1985). The conclusion to this paper discusses strengths and weaknesses of centrality indices when applied to affiliation networks.     

Are older adults networked individuals? Insights from East Yorkers’ network structure,relational autonomy,and digital media use

ABSTRACT

Networked individualism is a critical concept about the transition of the societal shift from geographically bounded local groups to the contemporary network society comprised of sparse, permeable, and dynamic communication networks. An underlying assumption about networked individuals thus far in the literature is that they are at a younger age. There are fears that older adults have been left behind in this transition to networked individualism. In this study, we are the first to inquire to what extent ? and in what ways ? are older adults networked individuals. Using in-depth interviews with 41 older adults living in the East York area of Toronto, we used a combination of quantitative coding, thematic analysis, and individual profiling to analyze their social network structure, relational autonomy, and digital media use. Our findings render a rather complex and nuanced picture, showing three types of older adults along the spectrum of networked individualism: networked individuals, socially connected but not networked individuals, and socially constrained individuals. Although most participants are socially connected, those who are networked individuals actively manage and navigate multiple, diverse, and non-redundant social networks. Digital media use is neither necessary nor sufficient in qualifying a person as a networked individual as the great majority of East Yorkers ? even if not networked individuals ? integrate digital media into their everyday lives.     

DOES WHOM YOU KNOW MATTER? UNRAVELING THE INFLUENCE OF PEERS' NETWORK ATTRIBUTES ON ACADEMIC PERFORMANCE

This paper examines how students' network size, distance, prestige, and connections to influential individuals impact academic performance. Larger and closer networks facilitate information exchange, but may also increase distractions that decrease productivity. We resolve this ambiguity using administrative data from a business school that features random assignment of students to multiple overlapping sets of peers, allowing us to calculate degree, closeness, eigenvector, and Katz‐Bonacich centrality for each node. We find that increasing closeness centrality within the network negatively affects student performance measured by grade point average, suggesting that synergy reduction and information processing costs outweigh benefits from greater information access. (JEL I23, L14, L23)     

ORGANIZED CRIME OR INDIVIDUAL CRIME? ENDOGENOUS SIZE OF A CRIMINAL ORGANIZATION AND THE OPTIMAL LAW ENFORCEMENT

This article develops a simple but general criminal decision framework in which individual crime and organized crime are coexisting alternatives to a potential offender. It enables us to endogenize the size of a criminal organization and explore interactive relationships among sizes of criminal organization, the crime rate, and the government's law enforcement strategies. We show that the method adopted to allocate the criminal organization's payoffs and the extra benefit provided by the criminal organization play crucial roles in an individual's decision to commit a crime and the way in which he or she commits that crime. The two factors also jointly determine the market structure for crime and the optimal law enforcement strategy to be adopted by a government. (JEL K4 )     

Mapping networks in corporate social responsibility communication on social media: A new approach to exploring the influence of communication tactics on public responses

Grounded in the networked stakeholder management theory and two-way communication, this study provides a snapshot of networks between companies and publics on Twitter in the context of corporate social responsibility (CSR) communication. Results showed that CSR communication activities (i.e., informing, retweeting, and mentioning) empowered a corporation through centralizing its network position and gaining public support (i.e., emotional, influencer, and knowledge support). In addition, degree centrality mediated the relationship between corporate retweets and stakeholder support and between corporate response and stakeholder support.     

Categorical attribute based centrality: E–I and G–F centrality

In a paper examining informal networks and organizational crisis, Krackhardt and Stern (1988) proposed a measure assessing the extent to which relations in a network were internal to a group as opposed to external. They called their measure the E–I index. The measure is now in wide use and is implemented in standard network packages such as UCINET ( Borgatti et al., 2002). The measure is based on a partition-based degree centrality measure and as such can be extended to other centrality measures and group level data. We explore extensions to closeness, betweenness and eigenvector centrality, and show how to apply the technique to sets of subgroups that do not form a partition. In addition, the extension to betweenness suggests a linkage to the Gould and Fernandez brokerage measures, which we explore.     

Social network structure and the trade-off between social utility and economic performance

We put forward a computational multi-agent model capturing the impact of social network structure on individuals’ social trust, willingness to cooperate, social utility and economic performance. Social network structure is modeled as four distinct social capital dimensions: degree, centrality, bridging and bonding social capital. Model setup draws from socio-economic theory and empirical findings based on our novel survey dataset. Results include aggregate-level comparative statics and individual-level correlations. We find, inter alia, that societies that either are better connected, exhibit a lower frequency of local cliques, or have a smaller share of family-based cliques, record relatively better aggregate economic performance. As long as family ties are sufficiently valuable, there is a trade-off between aggregate social utility and economic performance, and small world networks are then socially optimal. We also find that in dense networks and trustful societies, there is a trade-off between individual social utility and economic performance; otherwise both outcomes are positively correlated in the cross section.     

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.     

The application of network analysis to criminal intelligence: An assessment of the prospects

This paper explores the opportunities for the application of network analytic techniques to the problems of criminal intelligence analysis, paying particular attention to the identification of vulnerabilities in different types of criminal organization — from terrorist groups to narcotics supply networks.A variety of concepts from the network analysis literature are considered in terms of the promise they hold for helping law enforcement agencies extract useful information from existing collections of link data. For example, six different notions of “centrality” and the three major notions of “equivalence” are examined for their relevance in revealing the mechanics and vulnerabilities of criminal enterprises.     

Centrality and power revisited: actor success in group decision making

For many years, network analysts viewed positional centrality as a source of social power. More recently, laboratory studies of exchange networks have called the centrality–power link into question: under zero-sum exchange conditions, the ability of certain actors to directly exploit others has been found to account for power independent of actors' centrality. But most observers believe that in non-zero-sum communication networks, centrality should positively affect power. In this study we examine the effect of centrality on power in a communication network involving group voting on political issues. Using a model in which actors' votes are determined by the strength of their initial positions and the social pressures to which they are subjected, we conduct computer simulations to examine the extent to which actors in various network positions achieve favorable political outcomes. Our findings indicate that the link between centrality and power is highly contingent on the structure of the network. In networks with a central actor and an odd number of subgroups, central actors fail to dominate. In fact, in these networks, when peripheral actors are able to directly influence one another, the central actor becomes the least powerful in the network. In networks with a central actor and an even number of subgroups, however, the central actor dominates even in situations with connected peripherals. The highly contingent effect of centrality on power accords with the findings of exchange theorists who have studied power under zero-sum conditions. This raises questions about the nature of the distinction between communication and exchange networks.     

Ranking terrorists in networks: A sensitivity analysis of Al Qaeda's 9/11 attack

All over the world, intelligence services are collecting data concerning possible terrorist threats. This information is usually transformed into network structures in which the nodes represent the individuals in the data set and the links possible connections between these individuals. Unfortunately, it is nearly impossible to keep track of all individuals in the resulting complex network. Therefore, Lindelauf et al. (2013) introduced a methodology that ranks terrorists in a network. The rankings that result from this methodology can be used as a decision support system to efficiently allocate the scarce surveillance means of intelligence agencies. Moreover, usage of these rankings can improve the quality of surveillance which can in turn lead to prevention of attacks or destabilization of the networks under surveillance.The methodology introduced by Lindelauf et al. (2013) is based on a game theoretic centrality measure, which is innovative in the sense that it takes into account not only the structure of the network but also individual and coalitional characteristics of the members of the network. In this paper we elaborate on this methodology by introducing a new game theoretic centrality measure that better takes into account the operational strength of connected subnetworks.Moreover, we perform a sensitivity analysis on the rankings derived from this new centrality measure for the case of Al Qaeda's 9/11 attack. In this sensitivity analysis we consider firstly the possible additional information available about members of the network, secondly, variations in relational strength and, finally, the absence or presence of a small percentage of links in the network. We also introduce a case specific method to compare the different rankings that result from the sensitivity analysis and show that the new centrality measure is robust to small changes in the data.     

Embedding time in positions: Temporal measures of centrality for social network analysis

Digital data enable researchers to obtain fine-grained temporal information about social interactions. However, positional measures used in social network analysis (e.g., degree centrality, reachability, betweenness) are not well suited to these time-stamped interaction data because they ignore sequence and time of interactions. While new temporal measures have been developed, they consider time and sequence separately. Building on formal algebra, we propose three temporal equivalents to positional network measures that incorporate time and sequence. We demonstrate how these temporal equivalents can be applied to an empirical context and compare the results with their static counterparts. We show that, compared to their temporal counterparts, static measures applied to interaction networks obscure meaningful differences in the way in which individuals accumulate alters over time, conceal potential disconnections in the network by overestimating reachability, and bias the distribution of betweenness centrality, which can affect the identification of key individuals in the network.     

Interorganizational network findings from a nationwide cardiovascular disease prevention initiative

ObjectivesTo use network analysis in order to evaluate the effectiveness of interorganizational networks in implementing policy, systems, and environmental interventions for cardiovascular disease prevention throughout the United States.MethodsEvaluators conducted an interorganizational network (ION) survey to examine information sharing and joint planning within organizational relationships in 15 community-based cardiovascular disease prevention partnership networks. Density and betweenness centrality scores at the node- and network-level were calculated for each partnership network using UCINET© network analysis software. Common data patterns were then extracted using a multiple case study format.ResultsNetwork density scores ranged from 0.50 to 1.00 (M = 0.84, SD = 0.14) for information sharing and 0.43–1.00 (M = 0.77, SD = 0.15) for joint planning. Centralization indices ranged from 0.00 to 0.11 (M = 0.04, SD = 0.03), and 0.00-0.17 (M = 0.06, SD = 0.05), respectively. Overall, 73.33 % of communities were successful in meeting their partnership goals.ConclusionsWhen planning and implementing interorganizational networks, high betweenness centrality and more hierarchically structured networks were identified as the most salient partnership characteristics to programmatic success. The network findings were triangulated with previously published qualitative data to provide context. These findings provide valuable insight on how national networks can be designed and leveraged to implement systematic community health projects.     

NATIONAL POLITICS ON TWITTER

The increasing use of Twitter by politicians, journalists, political strategists and citizens has made it an important part of the networked sphere in which political issues are publicly negotiated. The growing number of studies investigating the relationship between Twitter and politics supports this claim. To the knowledge of the authors, this is the first study that examines the interrelation of individuals on the basis of their professions, their topics and their connection to mass media. Taking the example of Austria, they developed a user-centred method that overcomes the limitations inherent to other approaches in this field. The different types of data they gathered – Twitter user data, 1,375 newspaper articles and manually coded 145,356 tweets – allowed them to perform several analyses which provided insights into the structure and topics of a national public Twittersphere. Their results show that the network formed by Austria's most relevant political Twitter users is dominated by an elite of political professionals but open to outside participation. The topic analysis reveals the emergence of niche authorities and the periodic divergence of the political discourse on Twitter with that of mass media. The article concludes with a summary of how these phenomena relate to political participation.     

Subordinating Myth: Latino/a Immigration,Crime, and Exclusion

Rhetoric about “crime‐prone immigrants” has contributed to increased enforcement of the U.S.‐Mexico border, the passage of punitive immigration laws, and state and local efforts to make life difficult for new arrivals. Yet scholarly research refutes the notion that immigrants commit more crime. How do we explain this glaring contradiction? By reviewing recent research on immigration, crime, and social control in the context of racial stratification, this article describes the criminalization of Latino/a immigrants in the U.S. as a subordinating myth; that is, a falsity used as part of a larger effort to misallocate material, political, and cultural resources. Four ways in which the criminalization of immigrants contributes to racial exclusion are discussed: the profiting from immigration detention; the political scapegoating of racialized immigrants; the degrading of racialized bodies in enforcement efforts; and the literal control of exploitable populations. The article concludes with a call to develop further our understanding of the relationship between immigration and crime from this critical perspective in order to subordinate the myth of immigrant criminality.