Multinationals' Offshore Operations, Tax Avoidance, and Firm‐Specific Information Flows: International Evidence
Bibliographic record
Abstract
Abstract Using a large sample of multinational enterprises ( MNE s) over the period 1999–2009, this study investigates whether and how offshore operations via offshore financial centers ( OFC s) impact the extent to which firm‐specific information is incorporated into stock price, relative to common information. Our analyses show that, irrespective of whether a firm is a Type I offshore firm (directly having headquarters registered in OFC s) or a Type II offshore firm (indirectly setting up subsidiaries in OFC s), the amount of firm‐specific information flowing into stock price is lower for offshore firms than for non‐offshore firms. We also find that as offshore firms become more aggressive in their tax avoidance strategies, their stock prices impound a lower amount of firm‐specific information relative to common information. Finally, we find that a strong offshore proclivity also deters firm‐specific information flows, thereby driving up stock price synchronicity. Our results suggest that the opaque and complex nature of business and financial transactions in OFC s, coupled with their institutional characteristics, that is, weak and flexible legal enforcement, zero or extremely low taxation, and low litigation risk, provide offshore firms with not only stronger incentives but also the opportunities and means to adopt opaque disclosure policies and aggressive earnings management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".