A New Global Tax Deal for the Digital Age
Bibliographic record
Abstract
The Organisation for Economic Co-operation and Development (OECD) is in the midst of a project intended to tackle the tax challenges arising from the digitalization of the economy. As initially laid out in its program of work released in May 2019, the goal is to develop consensus on a new taxing right that would allow countries to tax multinationals even in the absence of traditional physical presence. In this paper, the authors argue that upon inspection, the plan seems primarily focused on rebalancing taxing rights mostly among a number of OECD member states plus a few other key non-OECD states, and that, viewed from this perspective, the urgent effort to forge a new global tax deal for the digital age risks deferring a much-needed discussion on the broader distributive implications of the current global tax deal to some unspecified future time. The first part of the paper offers a brief survey of some of the main factors that prompted the OECD to turn its attention to this topic. The second part considers the origins and development of nexus in the international tax regime, showing why this concept is amenable to broad expansion. The third part examines the range of reforms currently under consideration, arguing that the framing on digitalization misses a necessary connection to other pressing international policy programs that are also under development, most notably a global commitment to building institutions that support sustainable economic development. The paper concludes with a prediction that on its current trajectory, the program of work on digitalization is likely to produce a new global tax deal that looks much like the old global tax deal, with a relatively modest redistribution of taxing rights among a few key states, thus missing an opportunity for meaningful reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".