Beyond Tax Avoidance: Offshore Firms’ Institutional Environment and Financial Reporting Quality*
Bibliographic record
Abstract
Abstract We explore how firms’ operations in Offshore Financial Centers (OFCs) through subsidiaries or affiliates affect the quality of financial reporting. Using a unique and large sample of firms that have headquarters in the 15 countries with the strictest legal regimes and have subsidiaries or affiliates in OFCs, we find that such firms exhibit lower financial reporting quality than comparable firms without OFC operations. We also find that as OFC characteristics become more prevalent, firms are more likely to engage in both accrual‐based and real earnings management. More importantly, after disentangling OFC characteristics into the opportunity for tax avoidance, regulation arbitrage and secrecy policies, we find that beyond tax avoidance, regulation arbitrage and the secrecy policies of OFCs significantly affect financial reporting quality. The causal effect of OFC operations is supported by the analysis of financial reporting quality when firms set up OFC operations. Our findings are robust to various additional tests addressing potential endogeneity issues. We conclude that the assessment of a firm's institutional environment must encompass the registration status of its subsidiaries or affiliates as well as its own.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".