Wage theft, or the withholding of wages from workers who are rightfully owed remuneration, has gone relatively unexamined by criminologists, even those who study white-collar and corporate crime. This study draws on available data on corporate crime—and wage theft specifically—to estimate the likelihood of publicly traded U.S. firms engaging in wage theft. I test several hypotheses drawn from theoretical perspectives on corporate crime using a sample of publicly traded U.S. firms—848 sanctioned for wage theft and a comparison group of 1,334 non-wage theft firms—to determine firm characteristics that are predictive of firms’ engagement in wage theft. Results reveal that larger firms and those operating in industries previously identified as problematic for worker exploitation are more likely to have been sanctioned for wage theft. I discuss the implications and limitations of the study, as well as offer suggestions for further criminological inquiry into the problem.
Russia’s invasion of Ukraine precipitated a swift reversal in Western attitudes toward oligarchic wealth: Russian elites, long embraced by Western institutions, became subjects of rapid sanctioning and deliberate estrangement. Drawing on expert interviews and primary documents, we argue that targeted sanctions against Russian oligarchs created a new form of global deviance. Such deviance is not permanent but contingent on geopolitical priorities; reflects ties to authoritarian power rather than individual wrongdoing; absolves sanctioning bodies of their own complicity with autocrats; is inconsistent across even allied countries; and enforced by private rather than state actors. We conclude that targeted sanctions operate as performative exercises of Western authority that draw moral boundaries and police access to global markets, further entrenching existing geopolitical asymmetries.
Cultural explanations are routinely invoked in regulatory, journalistic, and academic accounts of organizational scandals. Yet despite the broad consensus that organizational culture matters, organizational criminology has produced relatively little systematic work on the concept. By contrast, fields like management sciences, organizational psychology, and safety research have developed an extensive conceptual and empirical literature on the relationship between organizational culture and various forms of misconduct; whether these insights can be translated to the study of corporate and organizational crime is, however, an open question. The present paper addresses this question through an integrative review of the literature, evaluating associated empirical outcomes of four organizational culture constructs – ethical culture, safety culture, masculinity culture, and risk culture – against four dimensions of criminologically relevant misconduct: deviance, intentionality, intended target, and impact. It finds that, while these constructs are widely used in management research to explain wrongdoing, and associated with misconduct as operationalized in that field, the outcomes studied tend to fall short of the more deviant, intentional, and harmful behaviors that lie at the heart of organizational criminology. We argue that this represents an opportunity and outline a research agenda for adapting management science constructs and methods to study organizational misconduct.
Technical support frauds and other crimes operating out of India’s call centers targeting victims in the Global North are a significant global problem. Drawing on neocolonial theory, and empirical analysis of literature and interview data, we examine how these frauds are organized and perpetrated. We reveal the profile of the primary victims and offenders, the motivations and the tactics used by the fraudsters, and the difficulties in combating such frauds. We show that exploitative outsourcing practices, and the allure of quick financial gain, foster a culture within the Indian call centers that encourages fraud. This paper has important policy implications for improving government responses to fraud, while arguing for addressing the socio-economic inequalities within globalized outsourced markets.
This study uses natural language processing and machine learning to classify financial fraud based on the SEC Accounting and Auditing Enforcement Releases disclosures. It introduces a hybrid framework combining Latent Dirichlet Allocation topic modeling and supervised learning to identify five fraud categories: financial misstatements, bribery, tax fraud, investment fraud, and related-party transactions. These are mapped to industry sectors to reveal contextual vulnerabilities. Logistic regression delivers stable, interpretable results, while deep learning models capture procedural fraud patterns. Chi-square tests and clustering confirm significant industry–fraud links, highlighting the need for sector-specific detection. By aligning outcomes with criminological theories, the study contributes to fraud analytics and regulatory enforcement. The findings support forensic accountants and analysts in building targeted, industry-aware fraud detection systems.
This study profiles and compares US and French subjects based on their attribution of blame for white-collar crime. 1,068 respondents (536 from the US and 532 from France) answered an online survey that measured their level of knowledge about white-collar crime, sociodemographic characteristics, and blame attribution styles. Cluster analysis was used to identify distinct and homogeneous categories of participants. More groups emerged in the US sample, suggesting more complex attitudes toward white-collar crime among American citizens. Subjects who were more knowledgeable tended to endorse a dispositional attribution style, and significant sociodemographic variation was found in blame attribution. This study confirms the importance of knowledge about white-collar crime in shaping overall attitudes toward it and potentially influencing punishment orientations.
This study applies machine learning (ML) techniques to predict fines imposed by the Mutual Fund Dealers Association of Canada on investment advisors who violate securities laws. Anchored in deterrence theory, the research evaluates whether fine allocation reflects proportionality, consistency, and severity. Using probabilistic and ML models with feature selection and extraction methods like PCA and RFE, the study identifies key predictors of fines. Results show that investigation costs and commissions are consistent predictors, while more serious offenses like quasi-criminal violations have limited influence. These findings raise concerns about regulatory leniency and the adequacy of current fine structures securities violation enforcement. The results from this study offers insights for a data-driven framework to improve fairness and effectiveness in regulatory enforcement.
The exponential rise of cryptocurrency has outpaced both understanding and safeguards regarding its utility, rendering crypto markets susceptible to fraud on a massive scale. In this paper, we seek to understand drivers behind female cryptocurrency purchasing behavior, as well as whether gender influences risk of victimization. Based on the analysis of a survey of over 900 cryptocurrency purchasers (33% female), this study explores the relationship between gender and a variety of influences related to cryptocurrency purchasing behavior. Our analysis revealed a significant relationship between gender and cryptocurrency knowledge as well as victimization. These findings have several implications, most crucially that female crypto purchasers may be differentially influenced by subcultural factors that increase risk of victimization compared to their male counterparts.
Much prior research has emphasized the importance of financial deregulation in encouraging crime by financial institutions. While the loosening of regulations is certainly a central factor, another significant element is the guardrail of bailouts. They protect negligent and even rule-breaking corporate officers who take on excessive risk. Bailouts, in fact, operate to enable decision-making, which would ordinarily lean toward risk-aversion, to be more risk-seeking.
Despite research and policy efforts over the years to develop a comprehensive data system for white-collar crimes, there has never been a published article that chronicles them. In this paper we describe these efforts, emphasizing specific findings and recommendations from our 2015 report, funded by the federal Bureau of Justice Statistics, on building a national database for corporate offenses. We summarize recently proposed Congressional legislation, based on our recommendations, to establish the development of a national database for tracking these offenses over time. Noting other recent moves within the federal government in this direction, we conclude that, despite the continuing lack of such a database, the trend towards it has been positive over the past several decades.
The Coronavirus Aid, Relief, and Economic Security (CARES) Act was passed in 2020 to provide financial assistance to Americans who suffered the economic effects of the COVID-19 pandemic. Part of this act was a Paycheck Protection Program (PPP) designed to provide forgivable loans to small businesses using capital for payroll expenses. Within a matter of days, abuses were alleged to occur. Loans were obtained to purchase luxury items by real and imaginary business owners. Prosecutors soon filed criminal complaints, and we collected 96 criminal cases between 2020 and 2022. We created a data set to analyze these abuses using insights from rational choice theory and provided an offender taxonomy.
White-collar crime (WCC) courses extend beyond the boundaries of one title and often involve expansive content coverage. Many scholars believe these areas are fundamental to criminology and criminal justice curricula. This research seeks to advance knowledge by exploring professors’ perspectives and examining syllabi to assist in identifying commonalities in academics’ views and underscoring pedagogical practices in WCC courses. The research compiled 32 undergraduate syllabi from members of the American Society of Criminology’s White-Collar and Corporate Crime Division who have taught the course to ascertain which textbooks are used, what prominent cases are reviewed, and how they engage students. Additionally, interviews with 23 professors were conducted to explore their teaching experiences. The findings highlight that while some standard practices are associated with WCC courses, each course is unique. Professors integrate a variety of assessments, reading materials, topics, and interactive activities in their courses. Four major themes emerged, including the frequency of course offerings, student investment, course content, and types of assignments.
This study examines the impact of gender on corporate and elite white-collar offending within the context of the Theory of Racial Privilege and Offending (TRPO). Using a survey of over 900 individuals, each responding to three vignettes of white-collar crime scenarios, we investigate whether the role of racial privilege suggested by TRPO differs by gender, which could help explain offending differences between males and females in white-collar crime. While the findings support gender differences in the creation of broad-cognitive frameworks, and in the impact of those frames on assessing ethicality of an act and willingness to commit the act, the differences were not always in the expected direction. This suggests a need for further research to understand these differences.
