Who, or What, Is to Blame for the Accumulation of Debt in Ontario and Quebec (and What Will It Take to Stop the Bleeding?)
Bibliographic record
Abstract
What is the main reason for government debt accumulation in Canada? Is the main driver of debt the public policy choices made by governments, or are non-policy factors, like interest rates and the economic environment to blame? Answering this question is the first step for governments burdened by high levels of government debt to introduce policies aimed at getting that debt under control. The effort to curtail debts in the mid-1990s prompted research into the sources of debt accumulation. The goal of this research was to determine whether the cause of debt was a set of poor fiscal policy choices in the form of overly generous social programs and/or insufficient taxation, an overly tight monetary policy driving up interest costs on existing debt and slowing growth, or simply bad luck in the form of unavoidable world events. That research aspired to identify the sources of debt accumulation so those mistakes, once identified, might be avoided in the future. This paper looks at Ontario and Quebec; two provinces with high and growing debt to GDP ratios and representing the two largest provincial economies in Canada. Introducing an original data set describing the finances of these governments over the period 1980-81 to 2011-12 and a new approach for identifying the causes of debt accumulation, this paper finds that the causes are disproportionate policy based. Finally, this paper offers a way out of debt for these governments. The solutions demand difficult policy choices; choices that will require a significantly heavier burden be borne by the citizens and taxpayers of Ontario than those in Quebec.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".