Incidence of gasoline taxes in Canada: an event study approach to estimating pass-through
Bibliographic record
Abstract
This study investigates the city-level pass-through rates of provincial and carbon tax changes on wholesale and retail gasoline prices in Canada from 1998 to 2024. Focusing on tax increases and decreases, the analysis measures pass-through rates to explore the impact of tax policies on gasoline prices across Canada. This study also analyses the tax incidence of medium/small cities and large cities separately to examine regional pass-through rates. Data is collected from Kent Marketing Ltd and Statistics Canada to create panel datasets for an event-based difference-in-difference model. Event plots show an immediate response in retail gasoline prices following tax changes; however, retail prices begin to adjust even before the implementation of tax policies. The estimated pass-through rate of gasoline tax increases on retail prices is 100% in the 9-week window and 88% in the 17-week window. For tax decreases, the pass-through rates are even higher, at 120% in the 9-week window and 140% in the 17-week window. The analysis also reveals the significant impact of tax changes on retail prices in medium/small cities and large cities. However, the study finds that the impact of gasoline tax changes on wholesale prices is statistically insignificant for both the 9-week and 17-week windows. Additionally, results from the 9-week window gasoline tax analysis without fixed effects and covariates are insignificant, underscoring the importance of accounting for these factors. In conclusion, the findings of this study emphasize the crucial role of provincial and carbon tax policies in shaping retail gasoline prices, while highlighting the limited impact on wholesale prices in Canada.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".