Big 4 offshore: Transparency arbitrage across legal and geographical boundaries
Bibliographic record
Abstract
Abstract How do global firms manage conflicting constituencies in complex markets? The Big 4 accounting firms have expanded their size and scope to the extent that they need to relate to different constituencies simultaneously, sometimes on controversial issues. This is particularly relevant given their engagement in aggressive tax planning services alongside their traditional professional obligations, as this generates a conflict between discretion offered to “offshore” clients and accountability offered to other stakeholders. This requires strategic duplicity—sending differentiated signals to different stakeholders. We suggest that firms use organizational partitioning across legal structures and geographies to enable strategic duplicity. We test this by collecting a unique data set on the Big 4's ownership structures and staff numbers across all locations, showing that their organizations are heavily segmented. We show that the Big 4 use this geographical and legal differentiation to send contrasting signals to constituents about their organizations, engaging in a type of strategic duplicity that we term transparency arbitrage, in which “onshore” stakeholders receive a signal of transparency and “offshore” stakeholders receive a signal of discretion. This duality enables them to engage in controversial issues with conflicting stakeholders.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".