Bibliographic record
Abstract
This thesis comprises three essays on the international movement of merchandise and people. The first essay measures the effects of foreign aid flows on a donor's merchandise exports. On average, donor countries tie approximately 50% of their foreign aid to exports, but the export stimulation of aid may exceed the amount that is directly tied. This essay uses the gravity model of trade to statistically test the link between aid and export expansion. The results suggest that aid is associated with an increase in exports of goods amounting to 120% of the aid. The essay also makes comparisons among donors and finds that Japan, which has drawn harsh criticism for using aid to gain unfair trade advantages, derives less merchandise exports from aid than the average donor. The second essay investigates the effects of immigration on Canada's pattern of trade. I derive three alternative functional forms capturing the relationship between immigration and trade based on the proposition that immigrants use their superior "market intelligence" to exploit new trade opportunities. I then employ province-level trade data with over 150 trading partners to identify immigrant effects and obtain results suggesting that immigrants account for over 10% of Canada's exports. The third essay addresses the question of whether tax differences contribute toward the brain drain from Canada to the U.S. This essay tests whether the U.S.'s lower taxes draw Canadians south by examining a sample of Canadians living in Canada and a sample of Canadians living in the U.S. Using information from these samples I estimate how much these individuals would earn in the opposite country and estimate the taxes they would pay. I find that the people who have the most to gain in income and in tax-savings are the most likely to choose to live in the U.S., and thus corroborate the claim that tax differences contribute toward Canada's brain drain.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".