Intra-Industry Trade between the United States and Canada
Bibliographic record
Abstract
In this thesis, US - Canada trade patterns were analyzed and the determinants of US - Canada Intra - Industry Trade (IIT) were empirically tested. IIT is explained using the new trade theories, including the Neo-factor Proportions Model and Monopolistic Competition Model under General Trade Equilibrium (instead of the Functional Hypotheses). The following three hypotheses that are empirically tested in this paper. The level of IIT is expected to be relatively high in industries: 1) with high levels of product differentiation, which is tested by the following proxies such as advertising expenses, value added and capital intensity, 2) typified by having economies of scale, which is tested by variable such as the average production cost; and 3) in which intense oligopolistic rivalry is common, where the oligopolistic rivalry is tested by proxy such as the world market share of US exports. Data were collected from the Organization of Economic Cooperation and Development (OECD) and the US Economic Census and proxies were developed to test each hypothesis. Three regression procedures were run. Results of the final model specification yielded statistically significant results and provided empirical evidence in support of the above three hypotheses. The findings resulting from this research include: First, the significant result of product differentiation variable - advertisement expenses, in manufacturing industries showed that advertisement expenses only significantly influenced the level of US - Canada IIT in manufacturing sector. This result is consistent with the observation that higher degrees of advertisement spending is associated with manufacturing industries because the existence of higher degrees of horizontal product differentiation in this sector as compared to other industry sectors. Large investment in advertisement is the direct result of high degree of horizontal product differentiation. Second, the regression results suggest that in the agricultural sector economies of scale is more likely to lead to comparative advantage in production. The greater economies of scale in agriculture sector result in a higher level of one-way trade, thus a lower level of IIT component of total trade. Third, industries with low capital intensity are more likely t? be linked with early stages of the product cycle and a low level of product differentiation. Therefore, a low degree of IIT should be observed in these industries. Fourth, the larger the international market share of US industries, the more international oligopolistic market power US companies have over foreign companies, the more difficult it is for Canadian products to enter US market. This leads to a low level of IIT in these industries. Finally, this research indicates that by mixing three and four digit SITC industries in one empirical study can cause misleading result, so it is critical to keep the same industry aggregation level for future empirical IIT study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".