The Economic Effects of Transportation Policy
Bibliographic record
Abstract
The Economic Effects of Transportation Policy a thesis submitted for the degree of Doctor of Philosophy and convocation year of 2021 by Ian Herzog to the Graduate Department of Economics in the University of Toronto. This thesis collects three papers discussing effects of transportation policy and infrastructure on neighbourhoods, cities, and regions. In each paper I combine new spatial data with economic theory to estimate how road pricing, traffic congestion, and road building affect economic wellbeing. In the first chapter, The City-Wide Effects of Tolling Downtown Drivers, I study the effects of London England's Congestion Charge on regional traffic, commuting, and economic activity's spatial distribution. I begin by showing that the policy reduced rush-hour traffic in the tolled downtown area and on radial roads leading downtown. I then use London's Congestion Charge as a natural experiment to find ensuing effects of traffic on commuting by car and public transit. These patterns calibrate a quantitative spatial model to conclude that London's Congestion Charge increases driving rates among untolled commuters and gives the region's commuters positive net benefits that disproportionately accrue to low-skill workers. In chapter two, The Marginal External Cost of Traffic in Greater London, I estimate the time costs of rush-hour road traffic in London England. To identify causal effects, I merge web-scraped travel times with Greater London's traffic monitoring network and compare across time of day at each road link. Results suggest that prevailing traffic conditions create a substantial deadweight loss, that driving downtown from the airport creates larger externalities than the average car commuter at the margin, and that public transit creates substantial congestion relief benefits. Chapter three, National Transportation Networks, Market Access, and Regional Economic Growth, steps back to estimate interregional transportation's effect on local economic activity by studying the Interstate Highway System. Using a new empirical strategy, I present evidence that through market access highways increased employment and had small and delayed wage effects. These patterns then calibrate a structural model to show that additions to early Interstate plans benefited places they cross but were less valuable than the system's core.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".