Analysis Of The Restrictions On Foreign Direct Investment In Free Trade Agreements
Bibliographic record
Abstract
The paper analyzes the quality of rules on foreign direct investment (FDI) for seven free trade agreements (FTAs): US-Australia, US-Singapore, Japan-Singapore, Korea-Singapore, NAFTA, Korea-Chile and Japan-Mexico, involving eight countries. We examine the quality of FDI rules in terms of their liberalization or restrictiveness in the following six areas: (a) restrictions on foreign ownership and market access, (b) national treatment, (c) screening and approval, (d) management and composition of board of directors, (e) entry of foreign investors, and (f) performance requirements. The results of our analysis revealed the following ranking from high to low quality, (1) US-Australia, (2) US-Singapore, (3) Japan-Singapore, (4) Korea-Singapore, (5) NAFTA, (6) Korea-Chile and (7) Japan-Mexico. Our analysis also revealed differences in the quality of FDI rules between and among the countries belonging to the same FTA, leading us to further investigate the quality at country levels. The analysis showed the following rankings, (1) US, (2) Singapore, (3) Australia, (4) Japan, (5) Korea, (6) Chile, (7) Mexico, and (8) Canada. The most salient feature of restriction was found on foreign ownership or the degree of participation that foreign investors can influence the enterprise. Among the sectors, the primary sector (especially, mining and agriculture) and services sector (especially, transportation, communications, electricity, financial and insurance) are very restrictive, while there are only a few restrictions on manufacturing sectors.
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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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".