JOURNAL ARTICLE
Technological Changes and Countries' Tax Policy Design: Evidence from Anti–Tax Avoidance Rules.
Published In: Management Science (INFORMS), 2025, v. 71, n. 3. P. 2192 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Brühne, Alissa I.; Jacob, Martin; Schütt, Harm H. 3 of 3
Abstract
This article investigates the association between technological changes and corporate tax policies, focusing on anti–tax avoidance rules, across 34 OECD countries from 1996 to 2016. Using a shift-share design based on exposure to U.S. technological advancements—measured via industry-level changes in total factor productivity, labor productivity, and capital intensity—the study finds that greater exposure to U.S. technological change is associated with tighter anti–tax avoidance rules, particularly in larger countries with higher exposure to intangible assets and stronger profit shifting incentives. The analysis distinguishes proactive policymaking, where countries anticipate tax avoidance risks from technological change, from reactive responses to observed tax avoidance, and shows stronger associations in countries with more long-term oriented cultures. The findings also reveal that smaller countries, facing higher risks of capital flight, do not exhibit this association, suggesting they may maintain looser anti–tax avoidance rules to attract investment, while larger countries use stricter rules to preserve tax revenues. The study does not find significant associations between technological change and statutory tax rates but observes a negative relation with investment incentives. Limitations include measurement constraints on technological exposure and anti–tax avoidance indices, and the inability to establish causal relationships.
Additional Information
- Source:Management Science (INFORMS). 2025/03, Vol. 71, Issue 3, p2192
- Document Type:Article
- Subject Area:Technology
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2021.03955
- Accession Number:183410370
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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