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Publications
Publications
  • 2016
  • Working Paper
  • HBS Working Paper Series

Who Pays for White-Collar Crime?

By: Paul Healy and George Serafeim
  • Format:Print
  • | Language:English
  • | Pages:50
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Abstract

Using a proprietary dataset of 667 companies around the world that experienced white-collar crime, we investigate what drives punishment of perpetrators of crime. We find a significantly lower propensity to punish crime in our sample, where most crimes are not reported to the regulator, relative to samples in studies investigating punishment of perpetrators in cases investigated by U.S. regulatory authorities. Punishment severity is significantly lower for senior executives, for perpetrators of crimes that do not directly steal from the company, and at smaller companies. While economic reasons could explain these associations, we show that gender and frequency of crimes moderate the relation between punishment severity and seniority. Male senior executives and senior executives in organizations with widespread crime are treated more leniently compared to senior female perpetrators or compared to senior perpetrators in organizations with isolated cases of crime. These results suggest that agency problems could partly explain punishment severity.

Keywords

Crime; Gender Bias; Women; Women Executives; Corruption; Legal Aspects Of Business; Firing; Human Capital; Human Resource Management; Prejudice and Bias; Crime and Corruption; Judgments; Law Enforcement; Human Resources; Corporate Governance; Gender

Citation

Healy, Paul, and George Serafeim. "Who Pays for White-Collar Crime?" Harvard Business School Working Paper, No. 16-148, June 2016.
  • SSRN
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About The Authors

Paul M. Healy

Accounting and Management
→More Publications

George Serafeim

Accounting and Management
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More from the Authors
  • David Crane’s Clean(er) Energy Strategy at NRG By: George Serafeim, Michael W. Toffel and Tom Quinn
  • FIJI Water: Carbon Negative? (Abridged) By: Michael W. Toffel, George Serafeim, Francesca Gino, Stephanie Van Sice and Tom Quinn
  • Machine Learning Models for Prediction of Scope 3 Carbon Emissions By: George Serafeim and Gladys Vélez Caicedo
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