JOURNAL ARTICLE
Does Taxing Business Owners Affect Employees? Evidence From A Change in the Top Marginal Tax Rate*.
Published In: Quarterly Journal of Economics, 2024, v. 139, n. 1. P. 637 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Risch, Max 3 of 3
Abstract
This article examines how an increase in the top marginal tax rate faced by business owners affected the earnings of their employees, using a novel linked owner-firm-worker data set derived from U.S. administrative tax records. Focusing on pass-through businesses—firms whose income is taxed at owners’ personal income tax rates—the study exploits the 2013 American Taxpayer Relief Act (ATRA) as a natural experiment and employs panel difference-in-differences methods to compare workers in similar firms with owners differentially exposed to the tax increase. The findings indicate that approximately 11–18 cents of each additional dollar of business income tax liability were passed through to worker earnings, primarily affecting the top 30% of earners within firms, while employment levels and firm productivity remained unchanged. The majority of the tax burden was borne by owners, resulting in a progressive effect that reduced after-tax earnings inequality between top-bracket owners and workers. The observed pass-through aligns with labor market models featuring rent sharing, and welfare analysis using a marginal value of public funds framework suggests that accounting for this pass-through moderately increases the estimated welfare cost of raising top marginal tax rates.
Additional Information
- Source:Quarterly Journal of Economics. 2024/02, Vol. 139, Issue 1, p637
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0033-5533
- DOI:10.1093/qje/qjad040
- Accession Number:174684289
- Copyright Statement:Copyright of Quarterly Journal of Economics is the property of Oxford University Press / USA 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.