The Interregional Incidence of Public Budgets in Federations: Measurement Issues, Evidence from Canada, and Policy Relevance
Bibliographic record
Abstract
In this paper, we examine the issue of the incidence of central government budgets in federal countries. In Section 1, we discuss a number of reasons why the picture painted of reality by even the best fiscal flow analysis is inevitably partial and hence inherently flawed to an unknowable extent. Despite these cautions, in Section 2 we review the evidence on the regional incidence of federal budgets in Canada, considering both aggregate results and some specific federal expenditure programs (e.g. equalization and employment insurance), as well as some relevant issues (e.g. the regional effect of some regulatory programs) not depicted in fiscal flows. We find that the regional distributional patterns revealed in this analysis are both robust to various reasonable adjustments and relatively stable over time. Nonetheless, we conclude in Section 3 that, while such studies are potentially useful in terms of providing a base-line for assessing performance in some respects, they cannot be used to demonstrate that e.g. one region is paying (or receiving) 'too much' or 'too little', let alone that there is a 'fiscal imbalance' that needs to be corrected. Numbers are necessary, and good numbers are better than bad ones; but they have to be interpreted carefully and in context before drawing any policy conclusions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".