Does tax enforcement disparately affect domestic versus multinational corporations around the world?
Bibliographic record
Abstract
Abstract Global tax enforcement policies have received increased attention since the financial crisis, with much stated focus on curbing perceived harmful tax practices of multinational corporations. Yet there is a dearth of evidence on possible differential effects of home‐country tax enforcement on multinationals. We take a step toward filling this void in the tax policy discussion by examining whether there is a differential relation between changes in home‐country enforcement and the tax avoidance of domestic versus multinational corporations. Using OECD data on 50 countries from 2005 to 2019, we find increases in home‐country enforcement are associated with lower levels of tax avoidance for domestic firms than for multinational corporations. Using a subset of firms from the Bureau van Dijk database, we find that multinationals avoid more tax in foreign countries when home‐country enforcement increases. Results are stronger for multinationals with a higher proportion of subsidiaries in low‐tax countries and when enforcement spending is low. These findings have implications for policy‐makers and highlight the importance of coordinated enforcement efforts across jurisdictions—such as the recently proposed global minimum tax—to successfully curb multinationals' worldwide tax avoidance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".