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This unique study explores the relationship between informal financial systems, illegal migration and human smuggling. Focusing on Chinese illegal immigrants working in the US, it examines the motivation and patterns of the use of illegal fund transfer systems, providing a revealing insight into the workings of Chinese underground banks

Smuggling people into industrialized countries has become a growing industry globally, and Chinese human smuggling represents a significant source of illegal immigration. The financing of the illegal immigration process, however, is a vastly under explored area. This book provides an in-depth understanding of the relationship between informal financial systems, illegal migration and human smuggling, tracing a trajectory of the evolution of Chinese underground banks in the US over the past two decades. With a focus on a border-spanning informal financial system from the perspective of their clients, this book elicits firsthand information from an extremely understudied population illustrating the motivation, patterns, and tendencies of their use of illegal fund transfer systems. Through extensive ethnographic fieldwork, this study illuminates the varied experiences of the undocumented Chinese immigrant population, providing revealing insights into the financial element of the migration and settlement process. Alongside discussion of policy implications for law enforcement in the US and China, Zhao explores whether Chinese underground banks represent a key mechanism in the larger social problem of illegal migration aided through human smuggling. Moreover, the book addresses the important question: is the Chinese underground banking system an integral part of organized crime, or is it a new type of ethnic-specific illegal enterprise?

This study applies the growing field of network science to explore whether police violence is associated with characteristics of an officer's social networks and his or her placement within those networks. To do this, we re-create the network of police misconduct for the Chicago Police Department using more than 38,442 complaints filed against police officers between 2000 and 2003. Our statistical models reveal that officers who shoot at civilians are often "brokers" within the social networks of policing, occupying important positions between other actors in the network and often connecting otherwise disconnected parts of the social structure between other officers within larger networks of misconduct. This finding holds, even net measures of officer activity, career movement, and sociodemographic background. Our finding suggest that policies and interventions aimed at curbing police shootings should include not only individual assessments of risk but also an understanding of officers' positions within larger social networks.

Background: It has become common practice to analyze randomized experiments using linear regression with covariates. Improved precision of treatment effect estimates is the usual motivation. In a series of important articles, David Freedman showed that this approach can be badly flawed. Recent work by Winston Lin offers partial remedies, but important problems remain. Results: In this article, we address those problems through a reformulation of the Neyman causal model. We provide a practical estimator and valid standard errors for the average treatment effect. Proper generalizations to well-defined populations can follow. Conclusion: In most applications, the use of covariates to improve precision is not worth the trouble.

There are over three decades of largely unrebutted criticism of regression analysis as practiced in the social sciences. Yet, regression analysis broadly construed remains for many the method of choice for characterizing conditional relationships. One possible explanation is that the existing alternatives sometimes can be seen by researchers as unsatisfying. In this article, we provide a different formulation. We allow the regression model to be incorrect and consider what can be learned nevertheless. To this end, the search for a correct model is abandoned. We offer instead a rigorous way to learn from regression approximations. These approximations, not "the truth," are the estimation targets. There exist estimators that are asymptotically unbiased and standard errors that are asymptotically correct even when there are important specification errors. Both can be obtained easily from popular statistical packages.