The Role of Corporate Social Responsibility in Solving the Great Corporate Tax Dodge
Bibliographic record
Abstract
In the wake of the Apple, Facebook, Starbucks, Amazon, and Google tax scandals, aggressive tax avoidance (ATA) by multinational companies (MNCs) has become the latest challenge for transnational tax policy. Tax avoidance, however sophisticated it might be, technically remains within the bounds of legality. States and tax agencies cannot impose a moral obligation to pay taxes beyond the obligations set out in law. However, there might be some scope for it in soft law, mainly through Corporate Social Responsibility (CSR). Part I of this Article will delve into the history of corporations and aggressive tax planning, how and why it came about, and what contributed to it. Part II will look briefly into what States and the international tax community have done so far to address the issue and what difficulties they have and will encounter in their fight against ATA. Part III will give some examples of current tax minimization strategies by MNCs and explain Google's Double Irish Dutch Sandwich to illustrate the complexity of these schemes. Part IV will be the core of this Article. It will attempt to explain, firstly, what is meant by CSR. It will provide an overview of the relationship between CSR and Tax Avoidance as it currently stands. It will show that corporations tend to increase their level of CSR activity when they engage in ATA practices as a way of hedging against the negative inference associated with such schemes. The Article will then examine why corporations ought to pay their fair share of taxes both from the State's perspective and from the corporation's perspective. It will put forward the benefit theory, the legitimacy theory, and the ability to pay principles as justifications for collecting corporate income tax. This Article will then consider why fair tax obligations ought to be made part of CSR through the lens of the three theories of the corporation: the aggregate theory, the artificial entity theory, and the real entity theory. Finally, it will look at some suggestions on how to incorporate fair tax obligations into CSR policy. Part V concludes that engaging in ATA practices is, in fact, counterproductive and that fair tax obligations should be part of a corporation's CSR agenda.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".