Unveiling Inequalities in the gig Economy: Analyzing driver earnings in Toronto’s ridehailing industry
Bibliographic record
Abstract
• Comprehensive analysis of 84 M trips and vehicle data to examine current state of TNC driver wages in Toronto. • 57% of TNC drivers earn below Ontario’s minimum wage of $16.55 per engaged hour. • Net TNC driver earnings per in-app hour were $7.94 in 2023 and $5.97 in 2024. • Policy solutions explored include utilization rate targets and minimum pay standards. • Findings are especially relevant as TNCs advocate for upfront pricing models. This study examines the net earnings of transportation network company (TNC) drivers in Toronto, using a comprehensive dataset of 84 million TNC trips conducted between January 2023 and April 2024. By integrating trip data with vehicle-specific information, the analysis offers a comprehensive view of driver earnings both before and after expenses. Findings reveal that in 2023, median gross earnings per engaged hour were $33.52, but net earnings dropped to $15.31 after deducting expenses, leaving 57% of drivers earning less than the provincial minimum wage of $16.55. When all in-app time is considered, net earnings declined further, with median earnings of $7.94 per in-app hour in 2023 and $5.97 in 2024, resulting in over 95% of drivers earning below minimum wage. These findings highlight systemic issues in the TNC industry, including prolonged unpaid waiting periods for ride assignments and significant variability in operating costs. We explore policy solutions such as utilization rate targets and minimum pay standards, alongside improvements to the data collection process, to better ensure fair wages for TNC drivers. By uncovering the prevailing wage conditions in the TNC industry, this research underscores the urgent need for proactive regulatory measures to enhance working conditions for TNC drivers in Toronto.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".