Essays on the economics of energy and transportation
Bibliographic record
Abstract
This dissertation is about the economics of energy and transportation, which has three chapters. The first chapter introduces traffic volume to control for the omitted variable bias and presents new estimates on the relationship between gasoline price and market density. A reduced-form approach is used to test for the relationship between market density and retail gasoline price with and without traffic volume. Furthermore, this chapter tests the potential relationship between price dispersion and market density with the introduction of traffic dispersion. I find that the omission of traffic volume biases the estimated effect of market density on retail gasoline price and leads to a 61% overstatement. In addition, traffic dispersion has a significant impact on price dispersion when a local market is defined by a 2km radius. Specifically, a local market with 50% higher measure of traffic dispersion would have a 3.43% higher measure of price dispersion. \n \nThe second chapter re-examines the impact of Uber rides on Yellow taxi trips using a different instrumental variable than Mammen and Shim (2018). In this chapter, unique dispatched vehicle of Uber is used to control for endogeneity. With the instrumental variable, I find that Uber rides have a significantly negative impact on Yellow taxi trips. Specifically, a one percent increase in the number of Uber rides would yield a 0.318% to 0.324% decrease in the number of Yellow taxi trips. This finding suggests that Uber rides significantly replace, rather than supplement, Yellow taxi trips. \n \nThe third paper evaluates the influences of lifting the U.S. oil export ban in a standard GTAP model. Using percent changes of the U.S. oil exports as shocks, I find that the removal of ban negatively impacted the motor gasoline industry in the United States. However, Latin America, a new importer of the U.S. oil, benefited from lifting the ban. Latin America has increased its motor gasoline production and export since 2015. In addition, the removal of the ban did not significantly impact the motor gasoline industry in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".