Inter-Nation Equity: The Development of an Important but Underappreciated International Tax Value
Bibliographic record
Abstract
Modern high-income states have relied on income taxes and redistributive spending to reduce inequality nationally; yet few states have considered how their national tax systems might have important implications for international income flows between high-income and low-income states and how their tax treaties might be used as a mechanism for achieving a fairer distribution of income internationally. Discussions about the possibilities of tax systems as a means of distributing income globally, and of acting in the service of the reduction of global inequality, are nascent, but not new. The foundational, and still leading, contribution to this area of scholarship is Peggy and Richard Musgrave's 1972 essay, 'Inter-Nation Equity'.\nThis contribution to the conference in honor of Richard Musgrave focuses on that 1972 essay, and the work that has built on it. Part 2 reviews the 1972 essay in some detail, highlighting the Musgraves' arguments and insights into the idea of inter-nation equity. Part 3 details Peggy Musgrave's subsequent contributions to our understanding of inter-nation equity. Part 4 turns to a consideration of some of the articles that have borrowed from and built on the Musgraves' work, and Part 5 offers a description of the state of our understanding about the content of inter-nation equity and provides some reflections on the increased importance of the concept.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".