Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 483

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Seeking Shelter: Empirically Modeling Tax Shelters Using Financial Statement Information

Open access
Lisowsky, Petro·2010·The Accounting Review

Abstract

ABSTRACT: Using confidential tax shelter and tax return data obtained from the Internal Revenue Service, this study develops and validates an expanded model for inferring the likelihood that a firm engages in a tax shelter. Results show that tax shelter likelihood is positively related to subsidiaries located in tax havens, foreign-source income, inconsistent book-tax treatment, litigation losses, use of promoters, profitability, and size, and negatively related to leverage. Supplemental tests show that total book-tax differences (BTDs) and the contingent tax liability reserve are significantly related to tax shelter usage, while discretionary permanent BTDs and long-run cash effective tax rates are not. Finally, the model is weaker, yet still significant, in the FIN 48 disclosure environment. This research provides investors and policymakers with an extended, validated measure to calculate the presence of extreme cases of corporate tax aggressiveness. Such information could also aid analysts and other tax and non-tax researchers in assessing the benefits and risks of firm behavior.

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 · 10.2308/accr.2010.85.5.1693

Cites

3 of 3 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 3 references

Cited by

See all 483

Metadata

Authors
Lisowsky, P.
Venue
The Accounting Review
Published
2010-09-01
Volume
85
Issue
5
Pages
1693-1720
ISSN
  • 0001-4826 (ISSN-L)
  • 1558-7967
OpenAlex
W3124108335
Cited by
483
References
3
FWCI
94.54
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: Seeking Shelter: Empirically Modeling Tax Shelters Using Financial Statement Information. https://app.cochat.ai/papers/10.2308/accr.2010.85.5.1693
The paper
Lisowsky, P. (2010). Seeking Shelter: Empirically Modeling Tax Shelters Using Financial Statement Information. The Accounting Review. https://doi.org/10.2308/accr.2010.85.5.1693

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.