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Who Blows the Whistle on Corporate Fraud?
Open accessAbstract
To identify the most effective mechanisms for detecting corporate fraud, we study all reported fraud cases in large U.S. companies between 1996 and 2004. We find that fraud detection does not rely on standard corporate governance actors (investors, SEC, and auditors), but rather takes a village, including several nontraditional players (employees, media, and industry regulators). Differences in access to information, as well as monetary and reputational incentives, help to explain this pattern. In‐depth analyses suggest that reputational incentives in general are weak, except for journalists in large cases. By contrast, monetary incentives help explain employee whistleblowing.
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Citations
Cited by
LEUZ, C., & WYSOCKI, P. D. (2016) · Journal of Accounting Research · doi:10.1111/1475-679x.12115 · Cited by 1,760
Do analysts matter for governance? Evidence from natural experiments
Chen, T., Harford, J., & Lin, C. (2015) · Journal of Financial Economics · doi:10.1016/j.jfineco.2014.10.002 · Cited by 747
Benmelech, E., & Frydman, C. (2015) · Journal of Financial Economics · doi:10.1016/j.jfineco.2014.04.009 · Cited by 734
How Media Coverage of Corporate Social Irresponsibility Increases Financial Risk
Kölbel, J. F., Busch, T., & Jancso, L. M. (2017) · Strategic Management Journal · doi:10.1002/smj.2647 · Cited by 682
Corruption culture and corporate misconduct
Liu, X. (2016) · Journal of Financial Economics · doi:10.1016/j.jfineco.2016.06.005 · Cited by 580
Metadata
- Authors
- DYCK, A., MORSE, A., & ZINGALES, L.
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 2010-11-09
- Volume
- 65
- Issue
- 6
- Pages
- 2213-2253
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W3121722682
- Cited by
- 1,858
- References
- 0
- FWCI
- 110.35
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
