The Timing and Direction of Statutory Tax Rate Changes by the Canadian Provinces
Bibliographic record
Abstract
Tax rate changes are some of the most significant and far-reaching decisions a government can take. A good understanding of the odds of any such changes is essential for any business debating the timing and location of investments. This paper investigates the factors that affect the timing of statutory tax rate changes by Canadian provincial governments. The authors develop a simple theoretical model to explain the “stickiness” of tax rates — the factors that lead a province to decide against tinkering with the tax system — based on the presence of fixed costs of adjusting tax rates. The results indicate that if the current rate falls within a range of tax rates bracketing the optimal rate, then the government will not adjust its tax rate because the cost of the reform outweighs the potential benefits. To build up a body of evidence, this paper employs a multinomial logit model to examine the likelihood of changes to personal income tax (PIT), corporate income tax (CIT), and provincial sales tax (PST) rates by provincial governments over the period 1973-2010. Regression results indicate that provincial governments that start with higher tax rates are more likely to cut, and less likely to raise, their tax rates. A higher provincial budget deficit reduces the probability of a CIT rate cut and raises the probability of a PST rate increase. Party ideology seems to matter. Provinces with leftleaning governments are less likely to cut PIT and PST rates, and more likely to raise PIT rates compared to non-left-leaning governments. The authors also find that a federal PIT rate cut raises the probability of a provincial PIT rate increase, whereas a federal CIT rate cut raises the probability of a provincial CIT rate reduction.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".