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Cited by 519

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Corporate Lobbying and Fraud Detection

Open access
Yu, Frank, Yu, Xiaoyun·2011·Journal of Financial and Quantitative Analysis

Abstract

This paper examines the relation between corporate lobbying and fraud detection. Using data on corporate lobbying expenses between 1998 and 2004, and a sample of large frauds detected during the same period, we find that firms’ lobbying activities make a significant difference in fraud detection: Compared to nonlobbying firms, on average, firms that lobby have a significantly lower hazard rate of being detected for fraud, evade fraud detection 117 days longer, and are 38% less likely to be detected by regulators. In addition, fraudulent firms on average spend 77% more on lobbying than nonfraudulent firms, and they spend 29% more on lobbying during their fraudulent periods than during nonfraudulent periods. The delay in detection leads to a greater distortion in resource allocation during fraudulent periods. It also allows managers to sell more of their shares.

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Metadata

Authors
Yu, F., & Yu, X.
Venue
Journal of Financial and Quantitative Analysis
Published
2011-06-06
Volume
46
Issue
6
Pages
1865-1891
ISSN
  • 0022-1090 (ISSN-L)
  • 1756-6916
OpenAlex
W3125484369
Cited by
519
References
2
FWCI
19.35
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Corporate Lobbying and Fraud Detection. https://app.cochat.ai/papers/10.1017/s0022109011000457
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Yu, F., & Yu, X. (2011). Corporate Lobbying and Fraud Detection. Journal of Financial and Quantitative Analysis. https://doi.org/10.1017/s0022109011000457

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.