Country-by-Country Reporting and Commercial Confidentiality
Bibliographic record
Abstract
Country-by-country reporting (CBCR) has been touted by the Organisation for Economic Co-operation and Development (OECD) as a possible reform effort to inhibit aggressive international tax planning that leads to revenue losses for high-tax countries. Under current accounting, tax-law, and securities-law regimes, multinational enterprises (MNEs) are generally not required to report to domestic tax authorities or disclose to the public any significant financial information concerning their operations in foreign countries. CBCR would change this environment so that MNEs would be required to annually report financial information, including revenue, profit before income tax, and income tax paid in respect of every country in which they operate. Under the current OECD proposal, MNEs will be required to disseminate this information to tax authorities on a confidential basis and will not be required to disclose any information to the public. This article evaluates, from a transaction cost perspective, the claim that reporting such information on a geographic basis could harm firm competitiveness if MNEs were also required to disclose such information to the public or if the information were improperly disclosed by foreign tax authorities to rival firms. While the empirical evidence on this issue is mixed, the analysis suggests that CBCR will not unduly raise MNE transaction costs, in part because there are sufficient legal protections to guard against the revelation of sensitive commercial or trade secrets. In fact, CBCR represents a transaction-cost-efficient reform that could inhibit the use of revenue-reducing international tax-planning strategies. The article additionally discusses transition issues with respect to the implementation of different "maximalist" or "minimalist" approaches to CBCR.
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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 | 0.066 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".