Digital Services Taxes, Tariffs, and Subsidies
Chris Noonan & Victoria Plekhanova
Abstract
This article examines digital services taxes (DSTs) from an international trade law and policy perspective, challenging prevailing narratives about their protectionist nature and offering alternative frameworks for their evaluation. The analysis begins by establishing that tariffs and discriminatory taxes on cross-border trade in goods and services remain permissible absent specific trade commitments, with many governments actively using these tools to advance a variety of policies. The article demonstrates that DSTs can serve legitimate policy objectives, and contrary to critics’ assertions, neither platform market characteristics nor implementation contexts support claims of protectionist intent. While acknowledging that digital platform markets often exhibit monopolistic tendencies, the article argues that DSTs’ structural features and inherent information asymmetries typically preclude their effective use as rent-snatching or profit-shifting instruments. Drawing on terms-of-trade theory, the analysis reveals why most nations would likely not benefit from a multilateral prohibition of DSTs, and how DSTs applied to digital platforms may enhance national welfare. The article proposes that, similar to the development of international rules relating to tariffs, rules will naturally evolve to coordinate DSTs, which may also reduce the risk of excessive taxation. Finally, the analysis introduces a novel perspective by examining DSTs through the lens of subsidy regulation, offering an alternative theoretical foundation for their implementation. This comprehensive analysis contributes to the scholarly discourse by providing a nuanced understanding of DSTs within the broader context of international trade and international tax regimes.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.