Rethinking the Division of Tax Room\nAnd Revenue in Fiscal Federalism
Bibliographic record
Abstract
The division of tax room for shared tax bases, such as income taxes in Canada, the United States, Australia, and Switzerland, is a frequent cause of political conflict between national and sub-national governments. Economists and legal scholars have developed a 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 article argues that the divergence between theory and actual practice can be partially explained by a 'credit assignment problem' that affects the political economy of government decision making. To illustrate, the article develops a model to identify the incentives governments face when dividing tax room. The model's central observations are that (a) the optimal division of tax room requires robust intergovernmental contracting, but (b) the necessary contracts are difficult or impossible to perform due to problems in allocating political credit – electoral rewards and punishments – between the two levels of government. The result is political conflict and, frequently, a sub-optimal division of tax room and the revenue that results. This article argues that law can perform two functions in responding to the credit assignment problem: (a) it can facilitate credit assignment, so that the necessary contracts over tax room and revenue are politically feasible, and (b), failing perfect credit assignment, it can mitigate the welfare effects of a sub-optimal division of tax room. The article shows how a credit assignment perspective should lead to reconsideration of constitutional law doctrines, such as the federal spending power and various doctrines that enable or limit concurrent expenditure jurisdiction. While these doctrines are the subject of long-running debates in legal scholarship, the credit assignment perspective offers new insights and new doctrinal prescriptions.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| 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".