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Cited by 394

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

How Much Will Firms Pay for Earnings That Do Not Exist? Evidence of Taxes Paid on Allegedly Fraudulent Earnings

Open access
Erickson, Merle, Hanlon, Michelle, Maydew, Edward L.·2004·The Accounting Review

Abstract

We analyze a sample of firms accused of fraudulently overstating their earnings and examine the extent, if any, to which they paid additional income taxes on the allegedly fraudulent earnings. Based on restatements of current tax expense adjusted for the tax benefits of stock options, the evidence indicates that many firms included the overstated financial accounting income on their tax returns, thus overpaying their taxes in the process of inflating their accounting earnings. We estimate that the median firm sacrificed eight cents in additional income taxes per dollar of inflated pretax earnings. In aggregate, we estimate that the firms in our sample paid $320 million in taxes on overstated earnings of about $3.36 billion. These results indicate how far managers of firms are willing to go when allegedly inflating earnings.

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Crossref · 10.2308/accr.2004.79.2.387

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Metadata

Authors
Erickson, M., Hanlon, M., & Maydew, E. L.
Venue
The Accounting Review
Published
2004-04-01
Volume
79
Issue
2
Pages
387-408
ISSN
  • 0001-4826 (ISSN-L)
  • 1558-7967
OpenAlex
W3121661444
Cited by
394
References
35
FWCI
91.60
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: How Much Will Firms Pay for Earnings That Do Not Exist? Evidence of Taxes Paid on Allegedly Fraudulent Earnings. https://app.cochat.ai/papers/10.2308/accr.2004.79.2.387
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Erickson, M., Hanlon, M., & Maydew, E. L. (2004). How Much Will Firms Pay for Earnings That Do Not Exist? Evidence of Taxes Paid on Allegedly Fraudulent Earnings. The Accounting Review. https://doi.org/10.2308/accr.2004.79.2.387

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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.