JOURNAL ARTICLE

Does Financial Reporting for Income Tax Expense Affect the Timeliness of Goodwill Impairments?

  • Published In: Journal of Financial Reporting, 2025, v. 10, n. 1. P. 45 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: King, Zachary; Lynch, Daniel P.; Stomberg, Bridget; Utke, Steven 3 of 3

Abstract

We examine if financial reporting for income tax expense affects the timeliness of goodwill impairments. Goodwill impairments are material, but their timing is subject to managers' discretion. U.S. GAAP requires firms to test all goodwill for impairment, whereas tax laws generally do not permit impairment deductions and allow amortization for only some goodwill. When an impairment includes goodwill that is not tax-amortizable, firms obtain no financial statement tax benefits to offset the impairment's negative effect on GAAP net income, thereby increasing the effective tax rate (ETR). We predict and find that managers are more likely to delay impairments when the impairment of nontax-amortizable goodwill generates a material ETR increase. We estimate that goodwill impairments are 11 to 14 percent more likely to be delayed when they materially increase ETRs. Our findings suggest financial reporting for taxes potentially distorts the timeliness of goodwill impairments, informing the debate on goodwill accounting. Data Availability: Data are available from public sources cited in the text. JEL Classifications: G34; K34; M41. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Financial Reporting. 2025/03, Vol. 10, Issue 1, p45
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:2380-2154
  • DOI:10.2308/JFR-2023-005
  • Accession Number:184869463
  • Copyright Statement:Copyright of Journal of Financial Reporting is the property of American Accounting Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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