Bibliographic record
Abstract
This thesis includes three essays on empirical macroeconomics and trade.In the second chapter, we estimate returns-to-scale at the industry level using the longest consistent dataset available for the U.S. economy.Focussing on the data since the late 1980s, the average estimate is 1, implying constant returns.An intuitive identity linking returns to scale, the markup, and the profit rate, gives a small implied average gross and value-added markups of approximately 5 and 10 percent, respectively, given our measure of the profit rate, over the past 30 years.This gross markup estimate is significantly less than the average gross markup estimates in the recent literature ranging from 30 -40 percent during this period.Put differently, given our estimated profit rate, large markups imply strongly increasing returns, which are not evident in the aggregate data.The third chapter studies the effect of Free Trade Agreements (FTAs) on the dollar value of already exported products (the intensive export margins) as recent literature documents an ambiguous impact.I develop a framework that explains the source of ambiguous effects of FTAs on intensive export margins.I use 6-digit bilateral trade data and five FTAs to estimate the dynamic effects of the agreements on Canadian exports to its FTA partners.I divide the pre-agreement export basket into two groups, i.e., products at positive and negative intensive margins.My differencei First and foremost, I would like to express my sincere gratitude to my supervisor,
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".