Vertical Sharing and Horizontal Distribution of Federal-Provincial Transfers in Canada, 1983-2018
Bibliographic record
Abstract
The Canada health transfer (CHT), the Canada social transfer (CST), and the equalization program are the main pillars of intergovernmental transfers in Canada. These transfers aim to address the vertical and horizontal fiscal imbalances that arise within the Canadian federation. This article provides a framework for the decomposition of federal transfers into their vertical and horizontal components. The empirical analysis is carried out for the period 1983-2018, which is divided into seven subperiods for analytical purposes. The results for the most recent subperiod, 2015-2018, show that (1) vertical, horizontal, and surplus transfers account for 74.85, 24.27, and 0.88 percent, respectively, of the total federal transfers; (2) the federal transfers addressed nearly 77 percent of the initial horizontal fiscal inequalities; (3) the equalization program is the primary channel for reducing horizontal fiscal inequalities, accounting for 85 percent of the total horizontal transfers; and (4) the CHT and CST have effectively become a channel for vertical transfers, contributing little toward horizontal equalization. In this context, there is potential for reform in the federal transfer system. The author suggests that vertical fiscal imbalances could be reduced by transferring tax points to provinces instead of providing specific-purpose transfers. The author also argues that immediate reforms are required in the fiscal stabilization program to address the concerns of oil-producing provinces that face a revenue shortfall because of the decline in oil prices.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".