Is It Time to Raise the Gas Tax? Optimal Gasoline Taxes for Ontario and Toronto
Bibliographic record
Abstract
This article uses a representative agent model and Canadian data to calculate the optimal gasoline taxes for Ontario and the Greater Toronto-Hamilton Area (GTHA) in a second-best setting with pre-existing distortionary income taxes. The results suggest a second-best optimal gasoline tax of 40.57 cents per litre in 2006 Canadian dollars for the GTHA that is much higher than the current tax rate of 24.7 cents per litre, and also higher than recently proposed increases. The resulting value is insensitive to whether the additional revenue is used to reduce taxes on income or to incrementally fund increased public transit infrastructure (the Big Move plan). However, in the absence of a regional tax, the second-best optimal gasoline tax for Ontario as a whole of 28.51 cents per litre in 2006 Canadian dollars is slightly higher than the current tax rate and in line with proposed increases.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".