Bibliographic record
Abstract
This thesis contains three essays that focus on topics in labour and urban economics. In Chapter 1, I use new administrative Canadian matched owner-employer-employee data to investigate the mechanisms that drive entry into entrepreneurial careers and entrepreneurial success among young individuals. I pay particular attention to the value of prior work experience in entrepreneurship. I use information on the career choices and earnings of individuals each year to structurally estimate a dynamic Roy model of career choice. I recover parameters governing: (a) the returns to various types of experience in the labour market and in entrepreneurship, (b) the non-pecuniary benefits associated with being a worker and an entrepreneur, and (c) career-specific entry costs. I use the estimated model to evaluate the impact of policies designed to promote successful entrepreneurship. Chapter 2 (joint with Leah Brooks and Gisela Rua) investigates how containerization impacts local economic activity. Containerization is premised on a simple insight: packaging goods for waterborne trade into a standardized container makes them dramatically cheaper to move. We use a novel cost-shifter instrument -- port depth pre-containerization -- to contend with the non-random adoption of containerization by ports. Container ships sit much deeper in the water than their predecessors, making initially deep ports cheaper to containerize. Consistent with New Economic Geography models, we find that cities near container ports grow an additional 70 percent from 1950 to 2010. Gains predominate in cities with initially low population density and manufacturing. Chapter 3 (joint with Marco Gonzalez-Navarro, Stefano Polloni, and Matthew Turner) investigates the relationship between the opening of a city's subway network and its air quality. We find that particulate concentrations drop by 4% in a 10km radius disk surrounding a city center following a subway system opening. The effect is larger near the city center and persists over the longest time horizon that we can measure with our data, about eight years. We estimate that a new subway system provides an external mortality benefit of about $594m per year. Although available subway capital cost estimates are crude, the estimated external mortality effects represent a significant fraction of construction costs.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".