Does Public Debt Matter? Empirical Evidence From Canadian Provinces
Bibliographic record
Abstract
It has always been a major priority for policy makers to know whether national debts are fiscally sustainable. The purpose of this research is to investigate the impact of public government debt levels (debt as a share of GDP) on per-capita GDP growth in the ten Canadian provinces. Using panel data from 1989 to 2008 and the standard two-stage least squares approach (IV/ 2SLS) as well as GMM estimation, the study concludes that public debt is inversely related to the growth rate of GDP per capita. Consistent with other existing studies, this paper also shows evidence that debt levels are nonlinearly associated to economic growth with a debt turning point estimated to be around 40% of GDP on average. This threshold may seem small compared to an average of 80-90% that has been reported in previous studies but it would be both irrelevant and misleading to make such a comparison. This is because the analysis in this paper is based on net financial debt (the difference between total financial assets and total liabilities) and not gross debt. Therefore, given how significant the gap between gross debt and net debt can be, the debt turning point estimated in this study is not small per se. Generally, this empirical work serves as a wake-up call to the provincial governments to start handling their fiscal management. Key words: Economic Growth, Per capita GDP Growth, Public Net Debt Canadian Provinces, Nonlinear.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| 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) | 0.007 | 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".