Opening up New Trade Routes for Financial Services: Canada’s Priorities
Bibliographic record
Abstract
The importance of services to Canada’s economy is often lost in the discussion of how Canada can take advantage of trade agreements such as the Trans-Pacific Partnership. In this Commentary, we look to close this gap with respect to the vital financial services sector. In order to determine the countries that Canada should target as realistic priorities in trade negotiations – with a focus on financial services – we ranked markets from the viewpoint of both economic attractiveness and the feasibility of concluding negotiations. We find that Canada’s first priority, which exploits Canada’s advantages in financial services, should be to ratify the TPP, as many of the countries ranked high on our list are involved in this agreement. Next, Canada should respond to China’s still outstanding offer to negotiate a trade agreement. In addition, we should build on our existing agreements and reinvigorate negotiations with Latin America, as well as with India, and engage with ASEAN nations such as Indonesia, the Philippines and Thailand. While not an exhaustive list, successful liberalization of financial services in these markets would bring significant gains to the Canadian financial sector and economy as a whole. This conclusion is supported by our empirical analysis of three liberalization scenarios – one the TPP as recently signed; second, a Canada-China comprehensive trade agreement that assumes, however, only minimal direct liberalization of financial services; and last an exercise in liberalizing only financial services with some key markets. From this wide range of scenarios, we find gains for Canada’s financial services sector to liberalizing trade. These gains come from the overall positive impact on economic growth of trade agreements, from any actual reduction to barriers affecting financial services, assumed to be fairly modest in all cases, and from the reduction of uncertainty that results from the “binding” of these barriers at levels much lower than what countries are allowed to impose under World Trade Organization rules.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".