Does fare-free transit increase labor-force participation and reduce income inequality?
Bibliographic record
Abstract
Fare-free transit policy is not new to several public transit systems and communities in the U.S. , as some local transit agencies have begun implementing fare-free transit policies or variations of them since the 1960s. Over time, the discussion regarding fare-free transit has been reignited by decreasing ridership trends in recent years and other thematic inquiries surrounding access, mobility and equity, operational efficiency, agency financial health, and community impacts. This research empirically investigates the effects of fare-free policy on transit ridership, labor force participation and income inequality . Using panel data regression models, we draw several conclusions: 1) Fare-free transit significantly increases ridership. 2) Fare-free transit neither significantly increases labor force participation rate nor reduces income inequality in small and medium-sized urbans. 3) Fare policy aside, external factors such as increased household income and work-from-home significantly reduce the demand for transit in small-urbanized areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".