Rethinking the Division of Tax Room in Fiscal Federalism
Bibliographic record
Abstract
The division of tax room for shared tax bases, such as income taxation in Canada and the United States, is a frequent cause of political conflict between national and sub-national governments. Economists and legal scholars have developed a normative theory that sets out an optimal division of tax room, but federal nations often substantially depart from this prescription by allocating too much or too little tax room to national governments. This thesis argues that the divergence between the normative theory and actual practice can be partially explained by three incentive problems that afflict the division of tax room: 1) a “credit assignment” problem; 2) a contract enforcement problem; and 3) a vertical tax competition problem. In combination, these problems produce an incentive structure that discourages the optimal division of room and invites high levels of political conflict. To illustrate, this thesis develops a model of tax room allocation, which it then applies to the four oldest constitutional federations: the United States, Switzerland, Canada, and Australia. The thesis argues that law can be used to improve the allocation of tax room in two ways. First, a number of non-tax legal doctrines, including rules addressing conditional grants, concurrent expenditure jurisdiction, direct cash transfers to individuals, and parliamentary sovereignty, should be revised to encourage consensual and welfare-maximizing exchanges of tax room between national and sub-national governments. Second, when consensual exchanges fail, law should be used to regulate vertical tax competition, so that the competitive process tends toward welfare-maximizing outcomes. This thesis examines a number of competition-regulating measures, including intergovernmental anti-discrimination rules and conflict-of-law rules. The thesis concludes by briefly considering whether and how these approaches can be applied to two other contexts in which vertical relationships are relevant: regional-local tax room allocation and international tax room allocation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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".