Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 328

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

The Effects of Regulatory Scrutiny on Tax Avoidance: An Examination of SEC Comment Letters

Open access
Kubick, Thomas R., Lynch, Dan, Mayberry, Michael A., Omer, Thomas C.·2014·SSRN Electronic Journal

Abstract

ABSTRACT This study examines the tax avoidance behavior of firms prior to the issuance, and following the resolution, of SEC tax comment letters. We find that firms that appear to engage in greater tax avoidance are more likely to receive a tax-related SEC comment letter. We also find that firms receiving a tax-related SEC comment letter, relative to firms receiving a non-tax comment letter, subsequently decrease their tax avoidance behavior consistent with an increase in expected tax costs. Additionally, we document evidence consistent with other firms that do not receive a comment letter reacting to multiple publicly disclosed tax-related comment letters within their industry by increasing their reported GAAP ETR, consistent with an indirect effect of regulatory scrutiny on certain types of tax avoidance.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2139/ssrn.2541035

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 54 references

Cited by

See all 328

Metadata

Authors
Kubick, T. R., Lynch, D., Mayberry, M. A., & Omer, T. C.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2014
Volume
91
Issue
6
Pages
1751-1780
ISSN
  • 0001-4826 (ISSN-L)
  • 1556-5068
OpenAlex
W3123401819
Cited by
328
References
54
FWCI
22.78
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Effects of Regulatory Scrutiny on Tax Avoidance: An Examination of SEC Comment Letters. https://app.cochat.ai/papers/10.2139/ssrn.2541035
The paper
Kubick, T. R., Lynch, D., Mayberry, M. A., & Omer, T. C. (2014). The Effects of Regulatory Scrutiny on Tax Avoidance: An Examination of SEC Comment Letters. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2541035

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.