Corporate Income Shifting in an Era of Tax Multilateralism: The Impact of Exchange-of-Information Agreements
Bibliographic record
Abstract
Using data from the annual reports of over 100,000 subsidiaries of multinational enterprises (MNEs) from 55 countries between 2003 and 2012, the authors of this article investigate the impact of exchange-of-information agreements ("EOI agreements") on tax-motivated income shifting. Transparency created by the signing of EOI agreements is expected to reduce the tax-motivated shifting of income by multinational corporations. Whether such agreements affect the income-shifting behaviour of multinational corporations is an unanswered question. The authors find evidence that, on average, EOI agreements do have an impact on tax-motivated income shifting. Additionally, they find that more advanced, modern EOI agreements are associated with a larger decrease in tax-motivated income shifting compared to the impact of early EOI agreements. This evidence challenges the prevalent assumption in empirical studies that EOI agreements are homogeneous. Supplemental analyses suggest that factors that affect the information asymmetry between MNEs and tax authorities, such as corporations with high levels of intangibles and tax authorities with strong transfer-pricing rules and enforcement, can diminish or enhance the effectiveness of EOI agreements in moderating tax-motivated income shifting. The evidence provided by this study shows that consideration of the tax authorities' information environment and the substance of an EOI agreement is essential when assessing the impact of such an agreement on the tax behaviour of sophisticated taxpayers such as multinational corporations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".