The impact of rising gasoline prices on U.S. public transit ridership’. Undergraduate honors thesis
Bibliographic record
Abstract
I would like to thank my advisor, Chris Timmins, and my honors seminar professor, Peter Arcidiacono, for their encouragement, insight, and patience, I certainly wouldn’t have made it to the end without their support. Joel Herndon and Sherry Chi were integral in helping compile the dataset. I would also like to thank my fellow 198S and 199S classmates for being helpful, supportive, and entertaining over the past year. Finally, I owe my parents much love and gratitude for giving me the opportunities to pursue my goals. 2 This paper analyzes the impact of increasing fuel prices on public transit ridership in the United States. Using regional gasoline prices and transit ridership and supply figures from 218 US cities from 2002 to 2008, I estimate the cross-price elasticity of demand for four modes of transit with respect to gasoline price. I report how these estimates vary between cities and test to see if these cross-price elasticities have changed over time. I find a cross-price elasticity of transit demand with respect to gasoline price ranging from-0.012 to 0.213 for commuter rail,-0.377 to 0.137 for heavy rail,-0.103 to 0.507 for light rail, and 0.047 to 0.121 for bus. These estimates vary significantly between cities but are not highly correlated with urban population size. Additionally, I find evidence suggesting that the cross-price elasticity has increased over this time period for commuter rail, light rail, and motorbus transit.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".