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Cited by 369

The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.

Taxes and Financial Constraints: Evidence from Linguistic Cues

Open access
LAW, KELVIN K. F., MILLS, LILLIAN F.·2015·Journal of Accounting Research

Abstract

Using a new measure of financial constraints based on firms’ qualitative disclosures, we find that financially constrained firms—firms that use more negative words in their annual reports—pursue more aggressive tax planning strategies as evidenced by: (1) higher current and future unrecognized tax benefits, (2) lower short‐ and long‐run current and future effective tax rates, (3) increase in tax haven usage for their material operations, and (4) higher proposed audit adjustments from the Internal Revenue Service. We exploit the unexpected closures of local banks as exogenous liquidity shocks to show that firms’ external financial constraints affect their tax avoidance strategies. Overall, the linguistic cues in firms’ qualitative disclosures provide incremental information beyond traditional accounting variables or commonly used effective tax rates to reveal and predict tax aggressiveness, both contemporaneously and in the future.

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Metadata

Authors
LAW, K. K. F., & MILLS, L. F.
Venue
Journal of Accounting Research
Publisher
Wiley
Published
2015-07-17
Volume
53
Issue
4
Pages
777-819
ISSN
  • 0021-8456 (ISSN-L)
  • 1475-679X
OpenAlex
W3124200290
Cited by
369
References
112
FWCI
73.68
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Taxes and Financial Constraints: Evidence from Linguistic Cues. https://app.cochat.ai/papers/10.1111/1475-679x.12081
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LAW, K. K. F., & MILLS, L. F. (2015). Taxes and Financial Constraints: Evidence from Linguistic Cues. Journal of Accounting Research. https://doi.org/10.1111/1475-679x.12081

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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.