Ownership and Use Taxes as Congestion Correcting Instruments
Bibliographic record
Abstract
In countries, such as Singapore, that have implemented vehicle congestion policies, recent years have seen a shift towards motor vehicle taxes based on car use. Ownership taxes reduce the number of cars on the road, leaving the price per trip largely unaffected. Use taxes such as fuel taxes and road use charges decrease the price of trips without necessarily penalising vehicle ownership per se. This paper presents a simple general equilibrium model involving trips from residential areas to a central business district, along with modal choice between cars and public transit. Car trips involve fixed costs but have lower variable costs per trip (including convenience costs) then bus trips. Using a calibrated numerical model, we investigate the relative merits of ownership and use taxes. We compare full internalisation of congestion externalities to optimal tax outcomes for the different tax types. In our framework, use taxes restore Pareto optimality since congestion damage rises with more trips. Ownership taxes only partially internalise congestion externalities. However, in terms of revenue-raising ability, the marginal excess burdens of ownership taxes in the neighbourhood of optimal taxes are typically lower than use taxes. This is because marginal increases in ownership taxes take away part of the surplus accruing to consumers who still choose to travel by car, and thus have less distortion at the margin.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".