Does Private Country‐by‐Country Reporting Deter Tax Avoidance and Income Shifting? Evidence from BEPS Action Item 13
Bibliographic record
Abstract
ABSTRACT To combat tax avoidance by multinational corporations, the Organisation for Economic Co‐operation and Development introduced country‐by‐country reporting (CbCr), requiring firms to provide tax authorities with a geographic breakdown of their profitability and activities. Treating the introduction of CbCr in the European Union as a shock to private disclosure requirements, this study examines the effect on corporate tax outcomes. Exploiting the €750 million revenue threshold for disclosure and employing regression‐discontinuity and difference‐in‐differences designs, I document a 1–2 percentage point increase in consolidated GAAP effective tax rates among affected firms. I also find evidence consistent with a decline in tax‐motivated income shifting, starting in 2018. These results suggest that, although private geographic disclosures can deter corporate tax avoidance, so far, the regulations have had a limited effect on tax‐motivated income shifting. My findings have policy implications for the global implementation of private CbCr and extend the debate on public versus private disclosure of tax information.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".