Non-Segmented Equilibria Under Differential Taxation: Evidence from the Canadian Government Bond Market *
Bibliographic record
Abstract
This paper investigates tax effects in the Canadian government bond market during the period 1964—1986. Unlike previous studies, we apply both statistical and nonstatistical teststo analyze clientele effects and market equilibria. The results divide the sample into two distinct periods of time, with the end of 1976 marking the division. We find that tax effects are almost nonexistent in the Canadian government bond market before the end of 1976, but are predominant in the post-1976 period. Non-segmented market equilibria cannot be rejected before 1977, but are strongly rejected after 1976. In fact, segmented equilibria with clientele effects in both quantities and prices characterize the entire five year period from 1982 to 1986. These findings are consistent with tax reforms, government deficit financing and interest rate fluctuations in Canada during our sample period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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; both teacher heads agree on what is shown here.
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".