The Law and Economics of Digital Taxation: Challenges to Traditional Tax Laws and Principles
Bibliographic record
Abstract
This article discusses the tax implications of information economics, the economic discipline that studies how information goods behave in a market economy. Information goods include all digital e-commerce goods and services: these goods typically involve high fixed costs of production along with almost zero marginal costs of reproduction and distribution. These characteristics of information goods present challenges to traditional tax laws and principles that emphasize control over (a) geographic space; (b) character of income; and (c) tangible goods and rights relating to them. If the digital economy becomes a significant part of overall economic activity, traditional tax laws may lead to unacceptable revenue losses, an uneven competitive playing field among different firms and market distortions resulting from the non-neutral tax treatment of digital economic activity vis a vis traditional economic activity. The article discusses how these challenges could be met by, among other things, broadening the tax base for consumption tax purposes and developing economic presence tests to replace physical presence tests for income tax purposes.
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".