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Record W7028364372

Essays on the economics of energy and transportation

2020· dissertation· en· W7028364372 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThinkTech (Texas Tech University) · 2020
Typedissertation
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsInstrumental variableOmitted-variable biasYield (engineering)Price dispersionMeasure (data warehouse)Dispersion (optics)Variable (mathematics)GasolineTRIPS architecture
DOInot available

Abstract

fetched live from OpenAlex

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.
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\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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.177
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it