Financial Transparency to the Rescue: Effects of Public <scp>Country‐by‐Country</scp> Reporting in the <scp>European Union</scp> Banking Sector on Tax Avoidance*
Bibliographic record
Abstract
ABSTRACT We analyze the effect of mandatory financial transparency on corporate tax avoidance. The effectiveness of comprehensive tax transparency, in the form of a public country‐by‐country reporting, to mitigate corporate tax planning is largely unknown. Capital Requirements Directive IV by the European Commission required multinational banks to publish key financial and tax data in the form of public country‐by‐country reporting. We examine tax avoidance of banks around the reform. Our focus is on multinational banks newly required to report activities in tax havens that had not been publicly disclosed before the country‐by‐country reporting mandate. We predict and find that these exposed banks increased their tax expense relative to multinational banks with no activities in tax havens to disclose, as well as relative to domestic banks unaffected by the new mandate. In additional tests, we compare our sample of exposed multinational banks to several control groups from the financial sector and other industries. Our results suggest that country‐by‐country reporting can serve as an additional policy instrument to curb corporate tax avoidance, but only when the reporting exposes the firms' tax sheltering activities to public scrutiny.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".