Fighting for Fares: Uber and the Declining Market Price of Licensed Taxicabs
Bibliographic record
Abstract
In this paper, we study how the emergence of Uber in a large North American city affects the financial value of taxicab licenses. A taxicab license provides a claim to a stream of dividends in the form of rents generated by operating the taxicab or leasing the license. The introduction of Uber undoubtedly affects the anticipated stream of dividends because Uber drivers capture part of the farebox revenue that might otherwise go to the owners/drivers of licensed taxicabs. At the same time, the launch of Uber's innovative technology-driven approach to the provision of ride-hailing services can be viewed as a partial obsolescence of the traditional taxicab approach. The economic incentives facing market participants may therefore change as Uber gains momentum in the ride-hailing market, which could further affect the market value of licensed taxicabs. Using transaction-level data, we apply a theory of asset pricing to the secondary market for Toronto taxicab licenses to explore these potential price effects. We learn that both the farebox and innovation effects contribute to the overall decline in market value, with the farebox effect accounting for just over half of the $170K price decline from 2011 to 2017. We explore the welfare implications for taxicab license owners with counterfactual simulations. We find that, consistent with the anti-Uber protests organized by Toronto taxi drivers, there was a high willingness to pay among license holders to prevent or postpone the launch of Uber's ridesharing services.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".