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Record W3124760268 · doi:10.1023/a:1011425214631

Non-Segmented Equilibria Under Differential Taxation: Evidence from the Canadian Government Bond Market *

2000· article· en· W3124760268 on OpenAlexaffabout
Alexandra E. Mackay, Eliezer Z. Prisman, Yisong S. Tian

Bibliographic record

VenueEuropean Finance Review · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsYork University
Fundersnot available
KeywordsEconomicsSample (material)Government (linguistics)Government bondDifferential (mechanical device)BondMonetary economicsPeriod (music)Bond marketInterest rateFinanceChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.068
GPT teacher head0.224
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2000
Admission routes2
Has abstractyes

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