How do investors value the publication of tax information? Evidence from the European public country‐by‐country reporting
Bibliographic record
Abstract
Abstract We examine the costs associated with public disclosure, as opposed to confidential reporting, of tax country‐by‐country reporting (CbCR) information. Our study addresses a critical knowledge gap, considering the growing adoption of public tax transparency measures. We aim to illuminate this matter by examining the expected costs for firms of making previously confidential CbCR information publicly available. The fact that the information was previously confidentially reported to the tax authorities allows us to assess the cost of publication in isolation. Employing an event study methodology, we provide early evidence on the capital market reaction to this new requirement on a sample of European firms falling within its scope. We document a significantly negative cumulative average abnormal return of EUR 47 billion to 64 billion for up to 3 days following the announcement. Additional cross‐sectional results suggest that concerns about the reputational costs arising from public scrutiny and the proprietary costs from disclosing sensitive business information outweigh the potential benefits of an extended information environment from an investor perspective. Our findings highlight that the public disclosure of tax information imposes significant—and likely unintended—costs from a firm perspective. This aspect should be carefully considered when developing tax transparency measures.
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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 | 0.011 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".