Essays in Quantitative Macroeconomics
Bibliographic record
Abstract
This thesis comprises three papers in quantitative macroeconomics that explore the following questions: (1) How does employer-provided training impact the college wage premium in the context of skill-biased technological change? (2) How does the option to sell a firm influence firm entry, exit, and growth dynamics? (3) How does college major selection impact occupational sorting and entrepreneurship? Chapter 1 combines matched employer-employee survey data from Canada with a quantitative model of the labour market featuring endogenous technology and training decisions to show that the rise in training, driven by technological advancements, attenuated the increase in the college wage premium by 63 percent between 1980 and the early 2000s. Chapter 2, co-authored with Bettina Brueggemann and Zachary Mahone, uses administrative matched employer-employee data from Canada and a quantitative model of firm dynamics to establish that transfers of business ownership significantly impact firm entry, exit, and growth dynamics, with 13 percent of new entrants surviving solely due to the option value of sale. Chapter 3 empirically establishes a negative relationship between STEM majors and entrepreneurship using micro-data from the 1997 National Longitudinal Survey of Youth. Through a quantitative model that links decisions regarding majors and entrepreneurship, I show that lowering STEM tuition increases STEM enrolment at the cost of reducing overall entrepreneurial activity.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".