The Effect of Trade Liberalization on Capital Markets- Case of Canada and U.S FTA, Sector Level Study.
Bibliographic record
Abstract
Transitional economies tend to see trade liberalization and capital reforms hand in hand. The main goal or aim for lawmakers is to maximize welfare effects when bringing trade and capital reforms within an economy. A nation gets greater access to international financial markets, which in turn attracts inflow of investments within economy. This newfound inflow of investments can also be attributed as a major contributor to a nations growth, leading to an upward push in the capital markets. The presented empirical study investigates if there exists a relationship in between Trade Liberalization and Capital Markets, at sector level over a period of ten years (1989- 1999). This paper investigates this relationship by using the case of Canada- USA free trade agreement (1988) and Toronto Stock Exchange Index (S&P/TSX). This being a relatively new topic in the field of research, only few past studies have been conducted. And they mostly rely on ‘Event Study’ methodology for their analysis. This empirical paper applies a different approach (i.e. Fixed Effects and Random Trend Model.) in analyzing the relationship between trade liberalization and capital markets. In this empirical study, we find in the case of Canada-USA FTA, TSX on average saw a fall of 0.22 percent, when tariffs rose by one percent. Enough the effect is negligible, but it is statistically significant as well. Though the model is not capable of explaining in the detail the cause of this effect, I believe in future study employment and firm size should also be included in the modeling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".