Unseen Equalization: Provincial Subsidies in Federal Programs
Bibliographic record
Abstract
In recent years, evidence has accumulated that regional subsidies incorporated in federal programs are substantial, affect all parts of Canada, and often have a direct impact on everyday life. These arrangements are not well understood because the funding directed to them is entwined with activities serving other purposes. Furthermore, the Government of Canada has never conducted a study of the economic impact of Canada’s comprehensive regional subsidy system. Consequently, with few exceptions, the resources being devoted to these programs, their impact on the economy, and the results being achieved are not evident to the public, governments, and legislators.This paper provides an overview of the interprovincial redistributive effects of six selected areas of federal programming incorporating regional subsidies. These areas have been chosen because of their size, because they are ongoing rather than one-time interventions, and because they represent different types of programming. They include: Employment insurance; Disproportionate federal employment as a subsidy; Airport ground rental arrangements; Regional economic development agencies; Cultural programs, and; Immigration settlement.Given that provincial subsidies can be built into programs as disparate as culture, airport rentals, and regional development, then it is probable that such subsidies are built into many other federal programs. The effect of many of the federal programs studied is to disproportionately support provinces east of the Ottawa River, and especially Atlantic Canada, at the expense of provinces west of it.The regional subsidy system appears to be so big — much bigger than equalization on its own — that it is not sustainable in anything close to its current form. As it stands, this system discourages growth and development in traditional equalization-receiving provinces. Regional subsidies encourage excessive infrastructure and excessively large provincial public services, and, in the case of EI, they discourage labour mobility. They also enable the growth of public sectors in traditional equalization-receiving provinces that are much larger than in Ontario, Alberta, or British Columbia. These factors all discourage growth and adversely affect the people of these provinces.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".