Public Corruption
Public Corruption
Embedded Files
Chen, Tong, and Yahong Zhang. "Unpacking Public Corruption in the U.S.: an Actor-Action-Target Analysis"
Chen, Tong, and Yahong Zhang. "Unpacking Public Corruption in the U.S.: an Actor-Action-Target Analysis"
Journal: International Public Management Journal
Journal: International Public Management Journal
DOI: https://doi.org/10.1080/10967494.2025.2567684
DOI: https://doi.org/10.1080/10967494.2025.2567684
Abstract: Prior corruption research in the United States has primarily focused on the causes and consequences of corruption through macro-level analysis of aggregated data. While they provide valuable insights into systemic and institutional drivers, micro-level descriptive research is essential for uncovering the patterns and vulnerabilities of public corruption, which are often obscured by aggregate data in macro-level analyses. This study introduces the Actor-Action- Target (AAT ) framework to systematically examine who engages in corruption (actor), what corrupt actions they undertake (action), and what targets are exploited for illicit gain (target). Hence, it provides a landscape of public corruption in the United States through a micro-level analysis of real public corruption cases. The dataset is obtained by applying generative AI to compile over 2,000 public corruption cases disclosed by the FBI from 2014 to 2025. The results reveal systematic patterns linking actors’ institutional authority to the forms of corruption committed and the target pursued. By highlighting these relationships, the AAT framework offers both conceptual understanding of public corruption and practical relevance, providing a structured basis for targeted anti-corruption strategies.
Chen, Tong, and Yahong Zhang. "Decompose the Scale of Corruption: An Analysis of Group Size and Monetary Benefit in Public Corruption"
Chen, Tong, and Yahong Zhang. "Decompose the Scale of Corruption: An Analysis of Group Size and Monetary Benefit in Public Corruption"
Journal: Public Integrity
Journal: Public Integrity
Status: R & R
Status: R & R
Abstract: Despite significant attention to the predictors of corruption in the government, research has predominantly focused on its prevalence and perception, often relying on aggregated indices and surveys that fail to capture the complexities of individual and group-level dynamics. Experimental studies, while valuable for testing hypotheses in controlled settings, are limited in replicating real-world conditions and generalizing findings, leaving the nuanced dimension of corruption underexplored. Utilizing data from public corruption cases in the United States, the study examines a theoretical framework for determining the scale of corruption, including the group size and monetary benefits. The analysis explores how individual and case-level factors influence the scale of the corrupt activities. It finds that corruption group size is primarily influenced by network dynamics and organizational environment, while monetary benefits received by corrupt officials are more strongly predicted by positional access to resources and the nature of the responsibilities. These insights suggest that corruption is not monolithic; instead, distinct mechanisms drive large-group and high-value cases. Anti-corruption strategies must, therefore, integrate preventive measures targeting network disruption and repressive measures deterring financial exploitation.
Chen, Tong. "How Long Can Corruption Hide? Determinants of Public Corruption Duration in the United States"
Chen, Tong. "How Long Can Corruption Hide? Determinants of Public Corruption Duration in the United States"
Journal: Public Administration Review
Journal: Public Administration Review
Status: Under Review
Status: Under Review
Abstract: This study examines the intricate dynamics of corruption networks in the U.S. public sector, focusing on the role of government employees and their collaborators in facilitating collective malfeasance. By leveraging Social Network Analysis (SNA), the research uncovers the structural and relational patterns within corruption schemes, highlighting how social ties and organizational relationships contribute to corrupt practices. Drawing on FBI press releases, the research maps corruption networks, distinguishing between formal and informal ties as well as strong and weak connections. AI tools are utilized to extract network features, uncovering patterns across cases based on scale, corruption type, and government level. The findings contribute to corruption research and policymaking by revealing the structural dynamics of corruption networks and their enforcement mechanisms, which hinder investigations and sustain malfeasance. By framing corruption as a collective social process, this study emphasizes the importance of network awareness and accountability in designing more effective anti-corruption strategies.
Page updated
Google Sites
Report abuse