Abusive Tax Avoidance and Responsibilities of Tax Professionals
Bibliographic record
Abstract
Abusive tax avoidance reduces the effectiveness and equity of fiscal institutions, and hence contributes to significant levels of deprivation in both developed and developing countries. In the first part of this paper, we outline the main reasons for the existence and scale of abusive tax avoidance, with emphasis on factors that exacerbate the problem in the developing world. However, our main project in this paper is normative. We argue that tax professionals, such as lawyers, accountants and financial advisors, have strong obligations to help remedy the deprivation caused by abusive tax avoidance. To make our case, we present three connective grounds that serve as criteria for remedial responsibilities: causal contribution, benefit and capacity to assist. Although these criteria sometimes pull in different directions, when all three converge there are especially strong grounds for assigning responsibilities to the relevant set of actors. Applying this convergence approach, we demonstrate that tax professionals contribute majorly to abusive tax avoidance, benefit greatly from its persistence, and have significant capacities to reduce its extent. One result of this analysis is that tax professionals—especially large accountancy, legal and securities firms—ought to do much more to address tax avoidance than merely comply with existing legislation. We also argue that these responsibilities are consistent with, indeed required by, widely accepted standards of professional integrity.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".