Essays on Trade, Economic Growth, Labor markets and Public policy
Bibliographic record
Abstract
This dissertation consists of four empirical chapters where I investigate the drivers of trade and migration within a country as well as examine the phenomenon of subnational income convergence and economic growth across countries. In Chapter 2 I estimate the size and pattern of barriers to the interprovincial trade in Canada. I simultaneously study how several regional agreements in place to eliminate those barriers impact trade within Canada. Using a panel of the Canadian provinces I find that most of these agreements have increased provincial trade whereas a couple of those deceased it. Also, there were large barriers to interprovincial trade that moderately decreased by 9.2 percent over a span of two decades in Canada. In Chapter 3 I measure the magnitude and pattern of barriers to the interprovincial migration in Canada. At the same time, I examine how various interprovincial agreements that have been negotiated to curb those barriers, influence the aggregate migration flows within Canada. I find that the average barriers to the interprovincial migration are larger than was previously estimated and are about one fifth the size of the average barriers to trade within Canada. Meanwhile the aggregate interprovincial migration responded perversely on average to the agreements examined. In Chapter 4 I explore what factors underlie the individual migration behaviour in Canada. I find that wage differentials, regulatory barriers, interprovincial agreements, and other explanatory variables had statistically significant but empirically small effects on the interprovincial migration of individuals. Finally, in Chapter 5 I investigate the per capita income convergence phenomenon as well as the determinants of the observed convergence pattern across the subnational jurisdictions in the North American Free Trade Agreement-NAFTA countries. I find that the poor jurisdictions grew faster than the rich ones and there was a decline in their income disparities after NAFTA. However, in the long run the jurisdictions converged either weakly or strongly in terms of their income growth under the various clubs that they formed both pre and post NAFTA. Such club formation was influenced by the initial conditions, structural and geographic characteristics some of which could be leveraged through government policies.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".