JOURNAL ARTICLE

The Relation between Payroll and Income Tax Avoidance.

  • Published In: Journal of the American Taxation Association, 2025, v. 47, n. 2. P. 75 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Marin, Michael J. 3 of 3

Abstract

Payroll taxes, such as contributions mandated through the Federal Insurance Contributions Act (FICA), are a considerable expense for businesses and a large source of government revenue. Despite the significant cost, little is known about the determinants of payroll tax avoidance. By misclassifying employees as independent contractors, firms can avoid their portion of FICA contributions and other employee-related costs. This paper uses publicly available Wage and Hour Division (WHD) compliance action data from the U.S. Department of Labor (DOL) to identify employee misclassification and examine whether firms that avoid income taxes also avoid payroll taxes. This study documents two main results. First, firms with higher CashETR, indicating lower income tax avoidance, are more likely to have Fair Labor Standards Act (FLSA) violations detected during a WHD audit. Second, firms increase their CashETR following the discovery of FLSA violations, indicating a reduction in income tax avoidance. Data Availability: The data that support the findings of this study are available from the U.S. Department of Labor's Wage and Hour Division. JEL Classifications: H25; H26; K31; K34. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of the American Taxation Association. 2025/09, Vol. 47, Issue 2, p75
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2025
  • ISSN:0198-9073
  • DOI:10.2308/JATA-2023-015
  • Accession Number:187639040
  • Copyright Statement:Copyright of Journal of the American Taxation Association 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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