Enhancing White Collar Crime Data Collection: A Feasibility Study on Federal Agency Enforcement Data

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Abstract

Efforts to measure white-collar and corporate crime in the United States have historically been hindered by fragmented and inconsistent data, limiting research and policy development. This article presents a feasibility study examining enforcement actions across five federal regulatory agencies—the FTC, CFPB, SEC, OSHA, and EPA—each with distinct mandates and enforcement mechanisms. Through a structured review of agency records, the study identifies key data elements necessary for comprehensive tracking, including case identifiers, offense and defendant characteristics, and outcomes. Challenges such as differentiating civil and criminal cases and ensuring data accuracy are addressed. While the study is limited to regulatory enforcement data and excludes federal criminal or civil prosecution records, findings demonstrate the viability of integrating case-level data across agencies. Additionally, the study highlights the utility of a hybrid data collection approach, combining manual review with artificial intelligence methods, to enhance data standardization and accessibility. These findings support the development of a centralized, standardized database to improve research on white-collar crime, strengthen enforcement practices, and inform policy initiatives. Further, this study demonstrates the utility of a hybrid approach using manual and artificial intelligence methods to collect, clean, and merge white collar crime data.

Authors

• Cristina Layana, Abt Global

• Shelby Hickman, Abt Global

• Vanessa Masick, Abt Global

• Sally Simpson, University of Maryland, College Park

• Isaiah Gerber, Abt Global