Do Government Budget Deficits Raise Bond Yields? Evidence from Canada
Bibliographic record
Abstract
This study applies quarterly data from 2013 through 2022 to a loanable funds framework to determine whether there is recent evidence that higher deficits and debt (both as a percentage of GDP) elevate the real yield on 10-year Canadian Treasury bonds. The study period is unique in that includes several quarters during which the COVID-19 pandemic was hampering the Canadian economy. Issues caused by the pandemic led the Canadian federal government to boost its spending by 70 % through $100 billion spending packages like the Canada Emergency Wage Subsidy and the Canada Emergency Response Benefit. Results from an autoregressive two-stage least squares regression suggest that larger Canadian federal government budget deficits and a higher debt-to-GDP ratio both elevated the real yield on 10-year Canadian government bonds. Other results indicate that the COVID-19 pandemic had its own positive impact on the real yield, potentially adding to any crowding out problems associated with higher real interest rates in Canada.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".