Taxation and Bond Market Investment Strategies: Evidence from the Market for Government of Canada Bonds
Bibliographic record
Abstract
This paper shows that, contrary to the suggestion of some investment advisors, for an individual Canadian investor subject to personal income taxation, the after-tax yield on a discount bond is always higher (or, at worse, equal) to the yield on a premium bond. This follows because the tax rate on capital gains is lower than the tax rate on coupon income in Canada. It is also shown that a decline in the capital gains tax rate raises the after-tax yield on discount bonds, but reduces the after-tax yield on premium bonds, and may even cause the yield on premium bonds to become negative. Further, a cut in the tax rate on interest income raises the after-tax yield on all bonds, but raises the yield on premium bonds relative to discount bonds. While the lower after-tax yields on higher coupon bonds might be expected to cause the pre-tax yields on these bonds to rise, no evidence of such tax capitalization is found using a large dataset of matched pairs of Government of Canada bonds for the period 1986-2006. The observed near equality of pre-tax yields since 1995 for bonds with different coupons implies that individuals in Canada earn a significantly smaller after-tax yield from holding premium bonds than discount bonds.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".